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Data Engineer

At DevSavant, we are a trusted technology partner specializing in Software Development, Data Engineering, AI/Machine Learning, Cloud Solutions, Automation Testing, and UI/UX Design. We deliver innovative, high-quality solutions with a focus on excellence and results. Our people are at the heart of everything we do, fostering a culture of growth and well-being. Join us and thrive in a supportive, success-driven environment.

We are looking for a Data Engineer with 3+ years of experience.

Responsibilities/ Impact:

  • Supporting Subscriber Metrics (SM) products by managing weekly and monthly pipeline deliveries. The automated, documented, and any scripted process for pipeline operation involves running existing playbooks and following automated/manual QA steps.

  • Assign a dedicated SM resource to become the long-term subject matter expert for all of current SM data supported products.

  • Expand company's responsibilities until they become the SM team capable of independently managing the entire SM data delivery process.

  • By delegating product delivery management, we'll free up high-value resources in all product departments so they may efficiently focus on expanding product capabilities for existing and new clients

Requirements:

  • Solid code generalist background with Python, shell scripting, GitHub, general configuration and code editing skills. 

  • Can support technical product delivery processes without needing the full depth of web application or pipeline development expertise.  

  • The role may grow into more responsibility, but for now the role is focused on helping us with core product delivery.

  • Collaboration with other team members like the designated Subscriber Metrics (SM) DataOps (DO) and Data Science (DS) teammate is essential.

Technical Skills:

Non Negotiable:

Python.

Cloud: AWS or GCP.

SQL.

Git.

Negotiables:

Shell Scripting.

YAML.

Data Pipelines experience.

DBT.

BigQuery.

Django.

React.

Average salary estimate

$75000 / YEARLY (est.)
min
max
$60000K
$90000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About Data Engineer, DevSavant Inc.

At DevSavant, we’re thrilled to announce an opening for a Data Engineer! If you have a knack for data and a passion for using technology to improve processes, this could be the perfect opportunity for you. With over 3 years of experience under your belt, you’ll play a key role in supporting our Subscriber Metrics (SM) products by managing weekly and monthly pipeline deliveries. Your day-to-day will involve running existing playbooks, following automated and manual QA steps, and ensuring that everything runs smoothly. You’ll also have the chance to become the go-to expert on our current SM data products and help us build a team that’s capable of independently handling the entire data delivery process. We cherish collaboration at DevSavant, so expect to work closely with our DataOps and Data Science teams. We are looking for someone with solid programming skills—especially in Python—as well as experience with AWS or GCP, SQL, and Git. Optional skills like shell scripting, YAML, and familiarity with data pipelines are a plus. Join us at DevSavant, where we believe that our people are the heart of our success and are committed to creating a culture of growth, innovation, and well-being. If you’re ready to take your data engineering skills to the next level and thrive in a friendly and supportive environment, we want to hear from you!

Frequently Asked Questions (FAQs) for Data Engineer Role at DevSavant Inc.
What are the responsibilities of a Data Engineer at DevSavant?

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.

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What qualifications do I need to apply for the Data Engineer position at DevSavant?

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.

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Can I grow into a more responsible role as a Data Engineer at DevSavant?

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.

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What technical skills are necessary for a Data Engineer role at DevSavant?

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.

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What type of team culture can I expect as a Data Engineer at DevSavant?

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.

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Common Interview Questions for Data Engineer
What experience do you have with managing data pipelines?

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.

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How comfortable are you with Python for data engineering tasks?

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.

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Can you describe a time when you collaborated with other teams?

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.

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What steps do you take to ensure data quality in your pipelines?

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.

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How do you stay updated with the latest data engineering trends and technologies?

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.

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What is your experience with cloud technologies, particularly AWS or GCP?

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.

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How do you approach debugging issues in data pipelines?

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.

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What coding tools do you prefer for version control?

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.

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Can you explain the importance of collaboration in data engineering?

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.

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What challenges do you anticipate in this Data Engineer role and how would you handle them?

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.

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