Data Engineer Jobs & Career Paths: Roles, Salaries, Expectations

With data lakes often quickly turning into swamps of unstructured data, data engineering is an illustrious job with a $125,000 yearly salary. However, it also has its fair share of challenges and frustrations related to the endless struggle of maintaining ETL pipelines. While data engineering is often grouped with other data-focused roles, it is ideally suited for those with a strong computer science background, proficiency in programming languages, and expertise in database management.

Organizations are constantly seeking analytics to fuel their data-driven decision-making efforts. Since you’re exploring data engineering roles, we’ve compiled essential details to give you a clearer picture of what to expect in the role and how to effectively advance your career.

Data engineers create the backbone infrastructure of the data lifecycle by designing, building, and maintaining data pipelines. They ensure that raw data is reliably transformed into a usable format, enabling data scientists, analysts, and business stakeholders to draw actionable insights.

As a data engineer, your primary responsibilities could include:

Key Responsibilities of a Data Engineer

1. Building Data Pipelines: Data engineers develop pipelines that move data from various sources into centralized storage, such as data warehouses or data lakes. These pipelines automate the extraction, transformation, and loading (ETL) processes.
2. Data Architecture Design: They design the architecture that underpins an organization’s data flow. This includes planning storage solutions, choosing the right databases, and structuring data to support scalability and efficient access.
3. Data Cleaning and Preparation: Data engineers ensure data is clean, consistent, and usable by removing duplicates, filling in gaps, and standardizing formats, which is essential for accurate analysis and reporting.
4. Database Management: They manage and optimize databases, including both SQL and NoSQL systems, to ensure high performance, security, and accessibility. Data engineers also consider debating the merits of SQL vs NoSQL databases as part of their job. I mean, who needs actual data work when you can just argue about databases all day, right? Just kidding!
5. Supporting Data Analytics and Machine Learning: By preparing data and ensuring it is accessible, data engineers support machine learning models and advanced analytics, making it easier for data scientists to extract insights and build predictive models.
6. Ensuring Data Quality and Security: Data engineers implement data governance practices, including data quality checks and security protocols, to maintain data integrity and compliance with regulations.

Some key skills and qualifications are critical to successfully performing your responsibilities as a data engineer. While the specifics may vary, here are some of the essential ones:

Programming and Scripting Languages

You’ll need to be highly proficient in SQL, as it’s essential for querying and manipulating relational databases. A strong understanding of complex queries, joins, and indexing is necessary.

Moreover, Python is a must for automating data processes, handling data manipulation, and working with libraries like pandas and NumPy. You should also be familiar with shell scripting to automate tasks within Unix/Linux environments.

Database Management

Your ability to manage and work with both relational and NoSQL databases will be a key part of your role. You’ll need to understand relational database systems like MySQL or PostgreSQL and be able to design schemas, optimize queries, and handle complex transactions.

In addition, your ability to model data efficiently and ensure its integrity across systems will be critical to maintaining reliable databases.

ETL/ELT and Data Pipelines

A major part of your job will be designing, building, and maintaining data pipelines for ETL data. Sometimes, you may work with ELT processes, where data is loaded before it’s transformed.

You’ll need to be proficient in using tools like Apache Airflow to orchestrate workflows and handle complex scheduling. Familiarity with both batch and stream processing is essential, as you’ll need to handle varying data ingestion requirements depending on the application.

Big Data Technologies

As a data engineer, you’ll likely work with big data technologies such as Apache Hadoop and Spark. You’ll need to understand how Hadoop’s HDFS works and how to leverage tools within the Hadoop ecosystem for large-scale data processing. Apache Spark will also be necessary for in-memory processing and analytics, so you should be comfortable using it to handle batch and real-time data processing needs.

Cloud Platforms and Infrastructure

Cloud computing will play a significant role in your day-to-day work as a data engineer. To manage data storage and processing, you’ll need to be proficient in at least one major cloud platform, such as AWS, Google Cloud, or Azure.

Data Warehousing

A data engineer must understand data warehousing solutions like Amazon Redshift, Snowflake, and Google BigQuery. These platforms allow for the efficient storage and querying of large datasets for analytics purposes. Depending on your company, you’ll need to understand how to optimize data models for performance and scalability, ensuring that the systems you build can handle complex queries and large volumes of data.

Data Quality and Governance

Ensuring the quality and integrity of data is an integral part of your job. You’ll need to implement processes to clean and validate data, removing duplicates and correcting errors. Familiarity with data quality tools will help automate these tasks. Additionally, you should understand data governance practices, including managing metadata, tracking data lineage, and ensuring compliance with data privacy regulations, especially when handling sensitive information.

Version Control and Collaboration

You’ll need to be proficient with version control systems like Git to manage and collaborate on code. Being able to work in a team environment, tracking changes, and maintaining version histories is essential for ensuring smooth collaboration. Additionally, understanding CI/CD pipelines will help automate the testing and deployment of your data systems, making your workflows more efficient and reliable.

Educational Background and Certifications

A bachelor’s degree in computer science, engineering, information technology, or a related field is typically required. However, hands-on experience and practical skills can sometimes be just as valuable as formal education. You may also pursue certifications from cloud platforms like AWS or Google Cloud or big data technologies like Cloudera or Databricks. These certifications can validate your skills and make you more competitive in the job market.

Soft Skills

While technical skills are central, you’ll also need strong problem-solving abilities to troubleshoot data issues and optimize systems. Good communication is equally important, as you’ll often collaborate with data scientists, analysts, and other teams to understand data requirements and deliver solutions. Project management capabilities will help you prioritize tasks, meet deadlines, and manage multiple projects simultaneously.

Data engineering is a multi-tiered career path that offers opportunities for advancement and specialization. Here are some of the most prominent data engineer roles and career levels that you may see in the industry:

1. Junior Data Engineer

It’s an entry-level position for individuals new to data engineering, often tasked with assisting in pipeline creation and maintenance.

• Salary: $53,000–$145,000
• Responsibilities: Assist in building and maintaining ETL pipelines, data cleaning, and basic troubleshooting.
• Skills Required: Proficiency in SQL and basic Python, familiarity with relational databases and cloud platforms.

2. Data Engineer

Data engineer is a mid-level role where engineers build, optimize, and maintain data pipelines and systems, handling a more extensive scope of projects.

• Salary: $83,000–$163,000
• Responsibilities: Build and optimize data pipelines, manage ETL processes, and ensure data quality for analytics.
• Skills Required: Advanced SQL, Python, cloud services, and knowledge of data warehousing and NoSQL databases.

3. Senior Data Engineer

Experienced engineers are handed this role to be involved in designing complex data systems and infrastructure for large-scale data needs.

• Salary: $81,000–$160,000
• Responsibilities: Design scalable data systems, manage complex workflows, and mentor junior engineers.
• Skills Required: Expert-level SQL and Python, cloud architecture, big data tools (Spark, Hadoop), and project management.

4. Data Engineering Manager

It’s a leadership role focused on managing data engineering teams, setting data strategy, and aligning projects with business objectives.

• Salary: $89,000–$212,000
• Responsibilities: Lead data engineering teams, align projects with business needs, and oversee compliance standards.
• Skills Required: Strong technical foundation, leadership, project management, and strategic planning.

5. Principal Data Engineer / Data Architect

This is a highly specialized role focused on designing and overseeing an organization’s entire data ecosystem and strategy.

• Salary: $100,000–$290,000
• Responsibilities: Architect end-to-end data systems, set data standards, and drive long-term data strategy.
• Skills Required: Expertise in data architecture, advanced cloud and big data knowledge, and cross-department collaboration.

Data engineering comes with high expectations and unique challenges associated with the roles. Here are some challenges you may face in your data engineering position:

Managing Constant Data Changes

Data is rarely static, and changes to source systems, business requirements, or upstream processes can disrupt pipelines. You must continually, and often mundanely, adjust ETL workflows to accommodate these shifts, which can lead to long hours of debugging and restructuring.

Data Swamps and Storage Overload

As hinted before, data lakes can quickly turn into “data swamps” when unstructured data accumulates without proper organization or governance. You’ll often face the challenge of implementing structure in a way that makes data accessible and useful while balancing storage costs and resource limitations.

Balancing Speed with Quality

Business teams often push for faster data access to support real-time analytics. However, rushing pipeline development can lead to issues with data quality, maintenance difficulties, or failures. You must strike a balance between speed and reliability, sometimes facing pressure to deliver faster than is sustainable.

Tech Debt from Quick Fixes

Under tight deadlines, you may need to apply quick fixes that solve immediate issues but create technical debt—a buildup of inefficiencies and workarounds that require significant time and effort to resolve later.

Legacy Systems and Integrations

Many organizations, especially non-FAANG companies, still rely on legacy systems that may not integrate well with modern data platforms. You might often need to find creative solutions to connect old and new systems, adding complexity to data integration.

High Expectations for Tool Mastery

There is pressure for data engineers to master a wide array of tools and technologies, from cloud services to big data platforms to stream processing systems. Staying current requires continuous learning and adaptation, often outside regular work hours.

Maintaining Work-Life Balance

Data engineering projects can be unpredictable, and urgent issues can arise unexpectedly, especially with ²⁴⁄₇ data pipelines. You may often find yourself managing on-call duties and working irregular hours to address issues quickly, which can impact work-life balance.

A career in data engineering offers exciting opportunities to build impactful data systems that drive decision-making across industries. With roles spanning junior positions to high-level architecture and management, data engineers can progress along a rewarding path with competitive salaries and clear advancement potential. All the best!