<h3>Job Description</h3><div>Job Description<div>Company Overview: Welcome to the forefront of data-driven innovation! Our company is dedicated to leveraging the power of big data to drive transformative change and solve complex problems across industries. We're committed to building cutting-edge big data solutions that enable advanced analytics, machine learning, and business intelligence. Join us and lead our team in shaping the future of big data engineering.Position Overview: As the Lead Big Data Engineer, you'll be at the forefront of our big data engineering efforts, leading a team of talented engineers to design, build, and maintain our big data infrastructure and pipelines. You'll drive the development of scalable and efficient data solutions that support the needs of our data-driven organization. If you're a seasoned engineer with expertise in big data technologies and a track record of leading successful data projects, we want you on our team.RequirementsKey Responsibilities:<ol><li>Technical Leadership: Lead a team of big data engineers, providing guidance, mentorship, and technical leadership in big data engineering best practices and technologies.</li><li>Big Data Infrastructure Design: Lead the design and architecture of our big data infrastructure, including data lakes, data warehouses, and streaming data platforms, ensuring scalability, reliability, and performance.</li><li>Data Pipeline Development: Lead the development of scalable and efficient data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data from diverse sources, ensuring reliability, scalability, and performance.</li><li>Data Modeling: Lead the design and implementation of data models and schemas to support analytical and operational requirements, ensuring data integrity, consistency, and performance in a distributed environment.</li><li>Data Integration: Lead efforts to integrate data from disparate sources and systems, ensuring data consistency, quality, and completeness throughout the data lifecycle.</li><li>Performance Optimization: Lead optimization efforts for data pipelines and queries, identifying and addressing bottlenecks and inefficiencies to improve system scalability and reliability.</li><li>Monitoring and Alerting: Lead the implementation of monitoring and alerting systems to track big data platform performance and health, detecting and mitigating issues proactively to minimize downtime and data loss.</li><li>Security and Compliance: Lead efforts to implement security controls and data governance policies to ensure data security, privacy, and compliance with regulatory requirements.</li><li>Documentation and Best Practices: Define and promote best practices for big data engineering, documentation, and usage, ensuring clear and comprehensive documentation to facilitate understanding and collaboration among team members.</li><li>Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to understand requirements and deliver big data solutions that meet business needs.</li><li>Mentorship and Development: Mentor junior engineers, providing guidance, support, and opportunities for growth and development in their big data engineering careers.</li></ol> Qualifications:<ul><li>Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related field.</li><li>7+ years of experience in big data engineering, with a focus on designing, building, and maintaining big data infrastructure and pipelines.</li><li>Leadership experience, with a demonstrated ability to lead and mentor a team of engineers.</li><li>Proficiency in programming languages such as Java, Scala, or Python, and expertise with big data technologies such as Hadoop, Spark, Kafka, Hive, HBase, and more.</li><li>Strong understanding of distributed systems and parallel processing, with experience designing and optimizing data pipelines for performance and scalability.</li><li>Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and familiarity with cloud-based big data services such as Amazon EMR, Azure HDInsight, or Google Dataproc.</li><li>Experience with SQL and NoSQL databases, data warehousing, and ETL/ELT processes.</li><li>Strong problem-solving skills and analytical thinking, with the ability to troubleshoot complex data issues and optimize system performance.</li><li>Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders.</li></ul>Benefits<ul><li>Competitive salary: The industry standard salary for Lead Big Data Engineers typically ranges from $200,000 to $300,000 per year, depending on experience and qualifications.</li><li>Comprehensive health, dental, and vision insurance plans.</li><li>Flexible work hours and remote work options.</li><li>Generous vacation and paid time off.</li><li>Professional development opportunities, including access to training programs, conferences, and workshops.</li><li>State-of-the-art technology environment with access to cutting-edge tools and resources.</li><li>Vibrant and inclusive company culture with opportunities for growth and advancement.</li><li>Exciting projects with real-world impact at the forefront of big data innovation.</li></ul> Join Us: Ready to lead the charge in big data engineering? Apply now to join our team and drive the future of data-driven innovation!</div></div>

Company Overview: Welcome to the forefront of data-driven innovation! Our company is dedicated to harnessing the power of data to drive transformative change and solve complex problems across industries. We're committed to building scalable and reliable data infrastructure that enables advanced analytics, machine learning, and business intelligence. Join us and lead our team in shaping the future of data engineering. Position Overview: As the Lead Data Engineer, you'll be responsible for leading our data engineering efforts and driving the design, development, and maintenance of our data infrastructure and pipelines. You'll lead a team of talented data engineers and collaborate closely with cross-functional teams to deliver end-to-end data solutions that support the needs of our data-driven organization. If you're a seasoned data engineer with expertise in data engineering technologies and a track record of leading successful data projects, we want you on our team. Key Responsibilities: <ol> <li> Technical Leadership: Lead a team of data engineers, providing guidance, mentorship, and technical leadership in data engineering best practices and technologies. </li> <li> Data Infrastructure Design: Lead the design and architecture of our data infrastructure, including data warehouses, data lakes, and data pipelines, ensuring scalability, reliability, and performance. </li> <li> Data Pipeline Development: Lead the development of scalable and efficient data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data from diverse sources. </li> <li> Data Modeling: Lead the design and implementation of data models and schemas to support analytical and operational requirements, ensuring data integrity, consistency, and performance. </li> <li> Data Governance: Establish and enforce data governance policies and best practices to ensure data quality, security, and compliance with regulatory requirements. </li> <li> Performance Optimization: Lead efforts to optimize data pipelines and queries for performance and efficiency, identifying and addressing bottlenecks and inefficiencies to improve system scalability and reliability. </li> <li> Monitoring and Alerting: Lead the implementation of monitoring and alerting systems to track data pipeline performance and health, detecting and mitigating issues proactively to minimize downtime and data loss. </li> <li> Documentation and Best Practices: Define and promote best practices for data engineering, documentation, and usage, ensuring clear and comprehensive documentation to facilitate understanding and collaboration among team members. </li> <li> Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to understand requirements and deliver data solutions that meet business needs. </li> <li> Mentorship and Development: Mentor junior engineers, providing guidance, support, and opportunities for growth and development in their data engineering careers. </li> </ol> Qualifications: <ul> <li>Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related field. </li> <li>7+ years of experience in data engineering, with a focus on designing, building, and maintaining data infrastructure and pipelines. </li> <li>Leadership experience, with a demonstrated ability to lead and mentor a team of engineers. </li> <li>Proficiency in programming languages such as Python, Java, or Scala, and experience with data engineering frameworks and tools such as Apache Spark, Apache Kafka, Apache Airflow, or similar. </li> <li>Strong understanding of data modeling concepts and techniques, with experience designing and implementing data models and schemas for relational and non-relational databases. </li> <li>Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and familiarity with cloud-based data services such as Amazon Redshift, Google BigQuery, or Azure Synapse Analytics. </li> <li>Experience with SQL and NoSQL databases, data warehousing, and ETL/ELT processes. </li> <li>Strong problem-solving skills and analytical thinking, with the ability to troubleshoot complex data issues and optimize system performance. </li> <li>Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders. </li> </ul><ul> <li>Competitive salary: The industry standard salary for Lead Data Engineers typically ranges from $200,000 to $300,000 per year, depending on experience and qualifications. </li> <li>Comprehensive health, dental, and vision insurance plans. </li> <li>Flexible work hours and remote work options. </li> <li>Generous vacation and paid time off. </li> <li>Professional development opportunities, including access to training programs, conferences, and workshops. </li> <li>State-of-the-art technology environment with access to cutting-edge tools and resources. </li> <li>Vibrant and inclusive company culture with opportunities for growth and advancement. </li> <li>Exciting projects with real-world impact at the forefront of data-driven innovation. </li> </ul>Join Us: Ready to lead the charge in data engineering? Apply now to join our team and shape the future of data-driven innovation! #J-18808-Ljbffr

Company Overview: Welcome to the forefront of data-driven innovation! Our company is at the cutting edge of leveraging real-time data to drive transformative change and solve complex problems across industries. We're committed to building cutting-edge real-time data solutions that enable timely insights and actions. Join us and lead our efforts in shaping the future of real-time data engineering. Position Overview: As the Lead Real-Time Data Engineer, you'll lead our initiatives in designing, building, and optimizing real-time data infrastructure. You'll spearhead the development of scalable, reliable, and efficient systems for ingesting, processing, and analyzing real-time data streams. If you're a seasoned engineer with expertise in real-time data technologies and a proven track record of leadership in delivering successful projects, we invite you to lead our team in this exciting opportunity. Key Responsibilities: <ol> <li>Technical Leadership: Provide strategic guidance, mentorship, and technical leadership to a team of real-time data engineers, fostering a culture of excellence, innovation, and collaboration. </li> <li>Real-Time Data Architecture: Lead the design and implementation of scalable and reliable architecture for real-time data processing and analytics, encompassing data ingestion, processing, and serving layers. </li> <li>Streaming Data Pipelines: Architect and develop robust real-time data streaming pipelines using technologies such as Apache Kafka, Apache Flink, or Apache Spark Streaming, ensuring low-latency and high-throughput processing. </li> <li>Event-Driven Architecture: Design event-driven systems to enable real-time processing of data events and triggers, supporting use cases such as real-time monitoring, anomaly detection, and alerting. </li> <li>Data Integration: Lead efforts to integrate real-time data streams from diverse sources and systems into the real-time data infrastructure, ensuring data consistency, integrity, and quality. </li> <li>Real-Time Analytics: Develop real-time analytics systems and dashboards to enable timely insights and decision-making, leveraging technologies such as Apache Druid, Elasticsearch, or Grafana. </li> <li>Data Governance: Implement data governance policies and procedures to ensure data quality, security, and compliance with regulatory requirements in real-time data environments. </li> <li>Performance Optimization: Optimize real-time data pipelines and processing workflows for performance, scalability, and efficiency, leveraging distributed computing and streaming processing techniques. </li> <li>Monitoring and Alerting: Implement robust monitoring and alerting solutions to track the performance and health of real-time data infrastructure and pipelines, proactively identifying and resolving issues. </li> <li>Documentation and Best Practices: Define and promote best practices for real-time data engineering, ensuring clear and comprehensive documentation to facilitate understanding and collaboration among team members. </li> <li>Collaboration: Collaborate closely with cross-functional teams, including data scientists, software engineers, and business analysts, to understand requirements and deliver real-time data solutions that meet business needs. </li> <li>Mentorship and Development: Mentor and coach junior engineers, providing guidance, support, and opportunities for skill development and career growth, and foster a culture of continuous learning and improvement within the team. </li> </ol>Qualifications: <ul> <li>Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or related field. </li> <li>8+ years of experience in data engineering, with a focus on real-time data technologies. </li> <li>Proven leadership experience, with a track record of successfully leading real-time data engineering teams and delivering complex projects. </li> <li>Expertise in real-time data streaming technologies such as Apache Kafka, Apache Flink, or Apache Spark Streaming. </li> <li>Strong programming skills in languages such as Python, Java, or Scala, with experience in distributed computing frameworks. </li> <li>Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and services like AWS Kinesis, Azure Stream Analytics, or Google Cloud Dataflow. </li> <li>Strong understanding of event-driven architecture and stream processing concepts, with experience building event-driven systems. </li> <li>Strong problem-solving skills and analytical thinking, with the ability to design and troubleshoot complex real-time data solutions. </li> <li>Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and communicate technical concepts to non-technical stakeholders. </li> </ul>Join Us: Ready to lead the charge in real-time data engineering? Apply now to join our team and be part of the data revolution! #J-18808-Ljbffr

Are you passionate about designing, building, and maintaining data pipelines that support robust data architectures and facilitate seamless data flow? Do you excel in creating scalable solutions that empower data-driven decision-making? If you’re ready to develop and optimize data systems that drive impactful analytics, our client has the perfect role for you. We’re seeking a Data Engineer (aka The Data Pipeline Architect) to build and manage cloud-based data infrastructures that support analytical needs and operational efficiencies.As a Data Engineer at our client, you’ll collaborate with data scientists, analysts, and software engineers to construct data pipelines and storage solutions that are both efficient and secure. Your role will be critical in ensuring data systems are optimized for performance, reliability, and scalability.Key Responsibilities:<ol><li>Design and Implement Scalable Data Pipelines: Develop and maintain data pipelines that support data ingestion, transformation, and integration using cloud technologies. You’ll automate data workflows and ensure the seamless movement of data between various systems.</li><li>Manage and Optimize Data Storage Solutions: Architect and maintain data lakes and data warehouses using platforms like BigQuery, Redshift, Snowflake, or similar cloud-based solutions. You’ll ensure data structures are built for performance and scalability.</li><li>Collaborate with Data Teams for Strategy Development: Work closely with data scientists, analysts, and business stakeholders to understand data requirements and align data solutions with business goals. You’ll provide input on data models and storage strategies.</li><li>Ensure Data Quality and Reliability: Implement and manage processes for data validation, error handling, and consistency checks. You’ll ensure the quality of data is maintained through robust testing and monitoring practices.</li><li>Develop and Automate ETL Processes: Build ETL (Extract, Transform, Load) workflows to handle complex data transformations. You’ll automate data extraction and transformation to support efficient data integration and reporting.</li><li>Monitor and Maintain Data Infrastructure: Use monitoring tools to track the performance and reliability of data systems. You’ll proactively identify and resolve potential issues to maintain system health and performance.</li><li>Optimize Data Processing and Resource Management: Implement strategies for efficient resource allocation and cost-effective data processing. You’ll leverage parallel processing and cloud capabilities to enhance performance.</li></ol>Required Skills:<ul><li>Cloud Data Platform Expertise: Experience with cloud data platforms such as AWS (Redshift, S3, Glue), GCP (BigQuery, Dataflow), or Azure (Azure Data Lake, Synapse). You’re proficient in handling cloud-based data solutions.</li><li>Programming and Scripting Knowledge: Proficiency in Python, Java, or Scala for building data pipelines and data processing tasks. You can write clean, efficient code for automation.</li><li>ETL and Data Pipeline Management: Proven ability to develop, maintain, and optimize ETL processes that handle large volumes of data. You’re experienced with orchestration tools like Apache Airflow or Luigi.</li><li>SQL and Database Management: Strong ability to write complex SQL queries and work with relational and NoSQL databases.</li><li>Problem-Solving and Critical Thinking: Excellent problem-solving skills with a proactive approach to identifying and resolving data-related challenges.</li></ul>Educational Requirements:<ul><li>Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. Equivalent experience in data engineering and cloud technologies may be considered.</li><li>Certifications in cloud data engineering (e.g., Google Professional Data Engineer, AWS Certified Big Data – Specialty, Microsoft Certified: Azure Data Engineer Associate) are a plus.</li></ul>Experience Requirements:<ul><li>3+ years of experience in data engineering, with a proven track record of building and managing cloud-based data systems.</li><li>Experience with real-time data processing frameworks like Apache Kafka or AWS Kinesis is advantageous.</li><li>Familiarity with containerization and microservices architecture is a plus.</li></ul>
#J-18808-Ljbffr

Are you passionate about building and optimizing data pipelines that drive data-driven decision-making in cloud environments? Do you have the technical expertise to design robust data architectures that manage large-scale data processing and ensure seamless data flow? If you’re ready to harness the power of cloud technology to create scalable data solutions, our client has the ideal role for you. We’re seeking a Cloud Data Engineer (aka The Data Pipeline Architect) to design, develop, and manage cloud-based data infrastructures that support analytical and operational needs across the organization.As a Cloud Data Engineer at our client, you’ll collaborate with data scientists, analysts, and software engineers to build data pipelines and storage solutions that are secure, efficient, and scalable. Your role will be vital in ensuring that data systems are optimized for performance and capable of supporting a range of data-driven initiatives.Key Responsibilities:<ol><li>Design and Build Data Pipelines: Create and manage scalable data pipelines that support ETL (Extract, Transform, Load) processes. You’ll develop automated solutions to handle data ingestion, transformation, and integration in cloud platforms such as AWS, GCP, or Azure.</li><li>Develop and Maintain Data Architectures: Architect and maintain cloud-based data storage solutions, such as data lakes and warehouses, ensuring they align with business needs and best practices. You’ll optimize data structures for performance and reliability.</li><li>Collaborate on Data Strategy and Solutions: Work closely with data scientists, business analysts, and application developers to understand data requirements and implement solutions that support analytics and machine learning models.</li><li>Ensure Data Security and Compliance: Implement and monitor security measures that protect data privacy and comply with industry standards (e.g., GDPR, HIPAA). You’ll manage data access controls and audit logs to safeguard sensitive information.</li><li>Optimize Data Processing Workflows: Improve the performance of data processing workflows by optimizing resource allocation and parallel processing techniques. You’ll work to reduce latency and enhance the scalability of data systems.</li><li>Automate and Streamline Data Operations: Develop automation scripts and tools using languages such as Python or Java to streamline data processes and reduce manual intervention. You’ll contribute to the efficiency and reliability of data operations.</li><li>Monitor and Troubleshoot Data Pipelines: Use monitoring tools and techniques to ensure the health and performance of data pipelines. You’ll identify and troubleshoot issues promptly to maintain smooth data flow and system uptime.</li></ol>Required Skills:<ul><li>Cloud Data Expertise: Strong experience with cloud data platforms (AWS, GCP, Azure) and data processing tools like Apache Spark, Apache Beam, or AWS Glue. You’re proficient in building scalable data solutions.</li><li>Programming and Scripting: Proficiency in Python, Java, or Scala for data manipulation and pipeline automation. You can create scripts that handle data transformation and integration tasks.</li><li>ETL and Data Pipeline Management: Experience in developing and managing complex ETL workflows that handle large data volumes. You’re skilled at building data pipelines that support real-time and batch processing.</li><li>Data Security and Compliance Knowledge: Familiarity with implementing data security measures and ensuring compliance with industry standards. You can configure access controls and monitor data usage.</li><li>Collaboration and Problem-Solving: Strong ability to work with cross-functional teams and troubleshoot data-related issues. You can clearly communicate technical concepts and solutions.</li></ul>Educational Requirements:<ul><li>Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field. Equivalent experience in data engineering and cloud environments may be considered.</li><li>Certifications such as Google Professional Data Engineer, AWS Certified Big Data – Specialty, or Microsoft Certified: Azure Data Engineer Associate are highly desirable.</li></ul>Experience Requirements:<ul><li>5+ years of experience in data engineering, with at least 3+ years focusing on cloud-based data solutions.</li><li>Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow, Dataflow, Step Functions).</li><li>Familiarity with database technologies such as SQL, NoSQL, and data warehousing solutions (e.g., BigQuery, Redshift, Snowflake).</li></ul>
#J-18808-Ljbffr

Are you passionate about handling massive datasets and building the infrastructure that enables complex data analysis and machine learning at scale? Do you excel in creating robust, scalable data pipelines that fuel data-driven decision-making? If you’re ready to tackle the challenges of big data, our client has the perfect role for you. We’re seeking a Big Data Engineer (aka The Data Pipeline Innovator) to architect and maintain high-performance data systems that empower analytics and support advanced data processing needs.As a Big Data Engineer at our client, you’ll collaborate with data scientists, analysts, and software engineers to design, implement, and optimize big data platforms. Your expertise in data engineering, distributed systems, and cloud infrastructure will be critical to ensuring that our data ecosystem is efficient, reliable, and scalable.Key Responsibilities:<ol> <li>Design and Build Scalable Data Pipelines:</li> </ol><ul><li>Architect and implement data pipelines for ETL processes using tools like Apache Spark, Kafka, and Hadoop. You’ll create data workflows that handle high-volume, high-velocity data and ensure seamless integration across systems.</li></ul> <li>Optimize Big Data Storage and Processing:</li> <ul><li>Develop and manage data storage solutions (e.g., HDFS, S3, Cassandra) that are optimized for performance and cost-efficiency. You’ll configure distributed processing systems to support efficient data retrieval and transformation.</li></ul> <li>Collaborate on Data Strategy and Integration:</li> <ul><li>Work closely with data scientists, analysts, and other engineers to align big data architecture with analytics goals. You’ll ensure data availability and integrity across systems to support business objectives.</li></ul> <li>Implement Data Quality and Governance Standards:</li> <ul><li>Develop processes and tools to monitor data quality and enforce data governance policies. You’ll ensure data is accurate, reliable, and secure through regular checks and validation processes.</li></ul> <li>Enhance Data Processing with Automation:</li> <ul><li>Use tools like Apache Airflow or AWS Glue to automate data workflows and reduce manual processing. You’ll implement scripts and automation that streamline data handling and improve efficiency.</li></ul> <li>Monitor and Troubleshoot Data Systems:</li> <ul><li>Use monitoring tools to track system performance and address issues proactively. You’ll troubleshoot and resolve any bottlenecks or failures to maintain optimal data processing capabilities.</li></ul> <li>Stay Updated on Big Data Trends and Technologies:</li> <ul><li>Keep up with advancements in big data technologies and tools. You’ll integrate new techniques and platforms that align with business needs and promote innovation.</li></ul> Required Skills:<ul> <li> Big Data Platform Proficiency: Extensive experience with big data technologies such as Apache Spark, Hadoop, Kafka, and Hive. You’re skilled at handling high-volume data and distributed processing.</li> <li> Data Pipeline and ETL Knowledge: Proven ability to design, build, and maintain ETL processes for massive datasets. You can handle both real-time and batch data processing requirements.</li> <li> Programming and Scripting: Proficiency in programming languages like Python, Java, or Scala for data processing and automation. Experience with SQL for data querying and manipulation is essential.</li> <li> Cloud Data Services Expertise: Familiarity with cloud platforms such as AWS, GCP, or Azure, including their big data and storage services (e.g., S3, BigQuery, Azure Data Lake).</li> <li> Data Quality and Governance: Strong understanding of data quality standards and governance practices, with experience in implementing data validation and monitoring frameworks.</li> </ul>Educational Requirements:<ul> <li> Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a related field. Equivalent experience in data engineering or big data management may be considered.</li> <li>Certifications in big data or cloud technologies (e.g., Cloudera Certified Data Engineer, AWS Certified Big Data – Specialty, Google Professional Data Engineer) are a plus.</li> </ul>Experience Requirements:<ul> <li> 5+ years of experience in data engineering, with at least 3+ years focusing on big data technologies and high-scale data environments.</li> <li>Experience in distributed systems and large-scale data storage management.</li> <li>Familiarity with containerization (Docker, Kubernetes) for deploying data processing environments is advantageous.</li> </ul><ul> <li>Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums.</li> <li>Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year.</li> <li>Work-Life Balance: Flexible work schedules and telecommuting options.</li> <li>Professional Development: Opportunities for training, certification reimbursement, and career advancement programs.</li> <li>Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources.</li> <li>Life and Disability Insurance: Life insurance and short-term/long-term disability coverage.</li> <li>Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges.</li> <li>Tuition Reimbursement: Financial assistance for continuing education and professional development.</li> <li>Community Engagement: Opportunities to participate in community service and volunteer activities.</li> <li>Recognition Programs: Employee recognition programs to celebrate achievements and milestones.</li> </ul>
#J-18808-Ljbffr

Data Engineer Unreal Gigs Jobs

Lead Big Data Engineer
December 17, 2024

Lead Data Engineer
December 9, 2024

Lead Realtime Data Engineer
December 9, 2024

Data Engineer The Data Pipeline Architect
December 9, 2024

Cloud Data Engineer The Data Pipeline Architect
December 9, 2024

Big Data Engineer The Data Pipeline Innovator
December 9, 2024

Cloud Data Engineer The Data Pipeline Architect
November 24, 2024

Data Engineer The Data Pipeline Architect
November 24, 2024

Quick filters

Top companies

Data Engineer Unreal Gigs Jobs

Lead Big Data EngineerDecember 17, 2024

Lead Data EngineerDecember 9, 2024

Lead Realtime Data EngineerDecember 9, 2024

Data Engineer The Data Pipeline ArchitectDecember 9, 2024

Cloud Data Engineer The Data Pipeline ArchitectDecember 9, 2024

Big Data Engineer The Data Pipeline InnovatorDecember 9, 2024

Cloud Data Engineer The Data Pipeline ArchitectNovember 24, 2024

Data Engineer The Data Pipeline ArchitectNovember 24, 2024