Eshares, Inc. is a leading platform dedicated to helping companies manage their equity, build businesses, and facilitate investment opportunities, aiming to create more ownership among individuals and organizations.
As a Data Engineer at Eshares, you will play a pivotal role within the Data Engineering team, responsible for designing and maintaining the company's data and analytics infrastructure. Key responsibilities include building resilient data pipelines that efficiently pull data from external and internal sources into the data warehouse, managing end-to-end projects that involve data modeling and visualization, and ensuring the smooth operation of the data infrastructure utilized by various teams across the organization. You will also lead infrastructure improvement initiatives, focusing on enhancing stability, observability, and automation, while collaborating with cross-functional teams to align on larger data initiatives.
To excel in this role, a strong background in data engineering with at least five years of experience is essential. Proficiency in building complex data and analytics infrastructure is critical, along with strong technical skills in Python, SQL, and experience with tools such as Airflow, dbt, Redshift, and Looker. The ideal candidate should possess excellent communication skills, be comfortable navigating ambiguity, and demonstrate a proactive approach to solving problems and implementing thoughtful solutions that advance business objectives. A passion for financial technology and familiarity with third-party integrations can be advantageous, though not required.
This guide will prepare you for your interview by providing targeted insights into the skills and experiences that Eshares values, allowing you to articulate your qualifications effectively and align with the company's mission and goals.
The interview process for a Data Engineer at Eshares, Inc. is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the collaborative and dynamic environment of the company. The process typically unfolds as follows:
The first step involves a 30-minute phone interview with a recruiter. This conversation serves as an introduction to the role and the company, allowing the recruiter to gauge your interest and fit for the position. Expect to discuss your background, relevant experiences, and motivations for applying. The recruiter may also provide insights into the company culture and the expectations for the role.
Following the initial screening, candidates are usually scheduled for a one-hour interview with the hiring manager. This session focuses on your technical expertise and past projects. You may be asked to elaborate on specific experiences related to data engineering, including your approach to building data pipelines and managing data infrastructure. Behavioral questions may also be included to assess your problem-solving abilities and how you collaborate with cross-functional teams.
Candidates who progress past the hiring manager interview will typically complete a technical assessment. This may involve a take-home assignment that tests your coding skills and understanding of data engineering principles. The assignment is designed to evaluate your ability to write clean, efficient code and may require you to demonstrate your knowledge of relevant technologies such as Python, SQL, and data modeling practices. Be prepared to spend several hours on this task, as it often requires thorough documentation and testing.
The final stage of the interview process usually consists of a series of onsite interviews, which may be conducted virtually. This phase typically includes multiple rounds focusing on both technical and behavioral aspects. You can expect to engage in discussions about your take-home assignment, participate in system design interviews, and answer questions related to your experience with data infrastructure and analytics. Additionally, you may have interviews with other team members to assess your fit within the team and the broader company culture.
Throughout the process, communication with the recruiter is key, as they will provide updates and feedback after each stage.
As you prepare for your interviews, consider the types of questions that may arise, particularly those that explore your technical skills and past experiences in data engineering.
Here are some tips to help you excel in your interview.
Before your interview, take the time to familiarize yourself with Carta's mission and values. The company emphasizes transparency, helpfulness, and leadership, which are crucial to their culture. During your interview, reflect these values in your responses and demonstrate how you align with their mission to create more owners. Engaging with the company’s all-hands meetings or reading their publicly available materials can provide valuable insights into their operations and culture.
Given the emphasis on building data pipelines and maintaining data infrastructure, ensure you are well-versed in the technologies mentioned in the job description, particularly Python, Airflow, dbt, and Redshift. Brush up on your SQL skills, as they are critical for data manipulation and querying. Be ready to discuss your experience with these tools and how you have used them in past projects. Consider preparing a few examples of how you have built or improved data pipelines, focusing on the challenges you faced and how you overcame them.
Expect to encounter questions that assess your ability to identify and solve infrastructure problems. Be prepared to discuss specific projects where you led initiatives to improve stability, observability, or automation. Use the STAR (Situation, Task, Action, Result) method to structure your responses, clearly outlining the context of the problem, your role in addressing it, and the outcomes of your actions.
As a data engineer, you will be working closely with cross-functional teams. Highlight your experience in collaborating with stakeholders, such as data scientists and product managers, to achieve common goals. Prepare examples that demonstrate your ability to communicate complex technical concepts to non-technical audiences, as this will be crucial in ensuring alignment across teams.
Expect behavioral questions that explore your past experiences and how you handle various situations. Prepare to discuss times when you faced conflicts with colleagues, how you prioritized tasks, and how you adapted to changes in project scope. Authenticity is key; share genuine experiences that reflect your problem-solving abilities and teamwork skills.
If you are given a take-home assignment, approach it with diligence. Pay attention to the details, such as code organization, documentation, and testing. The feedback from previous candidates indicates that clarity in your design decisions and adherence to best practices are critical. Treat this assignment as a real-world task, and ensure that your submission reflects your best work.
After your interviews, don’t hesitate to send a thank-you email to your interviewers. Express your appreciation for the opportunity to interview and reiterate your enthusiasm for the role. This not only shows professionalism but also keeps you on their radar as they make their decisions.
By preparing thoroughly and aligning your experiences with Carta's values and expectations, you can position yourself as a strong candidate for the Data Engineer role. Good luck!
In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Eshares, Inc. Candidates should focus on demonstrating their technical expertise, problem-solving abilities, and experience with data infrastructure and pipeline development. Be prepared to discuss your past projects, the technologies you've used, and how you approach collaboration with cross-functional teams.
This question assesses your hands-on experience with data pipelines and your problem-solving skills.
Discuss the specific technologies you used, the architecture of the pipeline, and any obstacles you encountered, along with how you overcame them.
“I built a data pipeline using Python and Airflow to automate the extraction of data from various APIs. One challenge was handling rate limits imposed by the APIs, which I resolved by implementing a backoff strategy to manage requests without exceeding limits.”
This question evaluates your familiarity with data modeling tools and your rationale for choosing them.
Mention specific tools you have experience with, such as dbt or Looker, and explain how they fit into your workflow.
“I primarily use dbt for data modeling because it allows for modular SQL development and version control. It integrates well with our data warehouse, Redshift, enabling efficient transformations and easy collaboration with the analytics team.”
This question focuses on your approach to data quality and validation.
Discuss the methods you use for data validation, monitoring, and error handling.
“I implement automated tests to validate data at each stage of the pipeline. Additionally, I use monitoring tools to track data flow and set up alerts for any anomalies, ensuring that issues are addressed promptly.”
This question assesses your familiarity with cloud infrastructure and services.
Highlight specific AWS services you have used and how they contributed to your data engineering projects.
“I have extensive experience with AWS, particularly with Redshift for data warehousing and S3 for data storage. I’ve utilized AWS Lambda for serverless data processing, which has significantly reduced our operational costs.”
This question tests your understanding of data processing methodologies.
Define both ETL and ELT, and explain the contexts in which each is preferable.
“ETL stands for Extract, Transform, Load, where data is transformed before loading into the target system. ELT, on the other hand, loads raw data into the target system first and then transforms it. ELT is often preferred in cloud environments where storage is cheaper and processing power is scalable.”
This question evaluates your teamwork and communication skills.
Provide a specific example that highlights your role, the team dynamics, and the outcome of the collaboration.
“I worked closely with the data science team to develop a predictive model. I facilitated regular meetings to ensure alignment on data requirements and provided insights on data availability, which ultimately led to a successful model deployment.”
This question assesses your time management and organizational skills.
Discuss your approach to prioritization, including any tools or frameworks you use.
“I use a combination of project management tools like Jira and the Eisenhower Matrix to prioritize tasks based on urgency and importance. This helps me focus on high-impact projects while ensuring that deadlines are met.”
This question tests your analytical thinking and problem-solving abilities.
Outline the steps you took to identify and resolve the issue, emphasizing your analytical skills.
“When I noticed discrepancies in our reporting data, I traced the issue back to a faulty transformation in our pipeline. I reviewed the logs, identified the error in the SQL code, and implemented a fix, which restored data accuracy.”
This question explores your passion for the field and your career aspirations.
Share your enthusiasm for data engineering and how it aligns with your career goals.
“I’m motivated by the challenge of transforming raw data into actionable insights. I enjoy the technical aspects of building robust data systems and the impact they have on decision-making across the organization.”
This question assesses your commitment to professional development.
Mention specific resources, communities, or courses you engage with to keep your skills current.
“I regularly follow industry blogs, participate in webinars, and am an active member of data engineering forums. I also take online courses to learn about new tools and best practices, ensuring I stay ahead in this rapidly evolving field.”