Data Engineer

As a Data Engineer at DevSavant, your key responsibilities include supporting Subscriber Metrics (SM) products by managing pipeline deliveries, running existing playbooks, and following QA steps. You’ll be tasked with becoming a subject matter expert on SM data supported products and ensuring the efficient management of the data delivery process.

To qualify for the Data Engineer role at DevSavant, you should have a solid background in coding, particularly in Python, along with experience in cloud services like AWS or GCP, SQL, and version control with Git. While a deep understanding of web application development isn’t needed, familiarity with shell scripting and data pipelines is an advantage.

Absolutely! The Data Engineer position at DevSavant is designed to evolve. Initially focused on core product delivery, you’ll have the opportunity to expand your responsibilities, potentially leading the management of the entire Subscriber Metrics data delivery process as you gain knowledge and expertise.

For the Data Engineer role, proficiency in Python, experience with cloud platforms such as AWS or GCP, SQL, and version control using Git are non-negotiable. Skills in shell scripting, YAML, and data pipelines (like DBT or BigQuery) are considered beneficial but not mandatory.

At DevSavant, you can expect a collaborative and supportive team culture where your growth and well-being matter. We encourage open communication and actively foster an environment where all members work together to achieve excellence and innovative solutions.

When discussing your experience with data pipelines, focus on specific projects where you successfully managed data flow. Describe the technologies and methodologies you used, such as ETL processes and tools like AWS or GCP, and any challenges you overcame during the project.

Be prepared to share specific examples of Python projects you’ve worked on, especially those aimed at data manipulation or pipeline development. Discuss your familiarity with libraries such as Pandas and how they contributed to your effectiveness in handling data.

Share a situation where you collaborated across teams, possibly with DataOps or Data Science, and highlight how effective communication led to project success. Focus on your role in facilitating that collaboration and any tools or strategies you used.

Explain your approach to data quality, emphasizing practices such as thorough testing, documentation, and the use of automated QA steps. Discuss how you’d monitor data integrity and address inconsistencies in your pipelines.

Demonstrate your commitment to professional development by discussing the resources you utilize, such as online courses, webinars, or industry publications, to stay current on trends in data engineering and how this knowledge informs your work.

Detail your experience with cloud technologies, specifying how you’ve utilized AWS or GCP for storage, computing, and data management. Provide examples of projects where your cloud expertise made a significant impact on the operation or efficiency of data processes.

Discuss your systematic approach to debugging, including identifying the error, using logs for diagnosis, testing different components of the pipeline, and documenting the solution for future reference. Provide examples of complex issues you have successfully resolved.

Mention your experience with Git, highlighting how you’ve used it for version control in your projects. Discuss your familiarity with repository management, branching strategies, and collaboration practices involving version control tools.

Reflect on the value of collaboration in data engineering roles by discussing how seamless teamwork can improve results and foster innovation. Provide examples of collaborative projects where diverse inputs led to better solutions.

Identify potential challenges like managing workload during peak project times and discuss strategies you would implement to prioritize tasks, communicate clearly with your team, and remain adaptable to changes.