Sr. Data Engineer

The Sr. Data Engineer at our San Francisco office is responsible for designing, maintaining, and evolving our data platform. Key responsibilities include owning and maintaining scalable data pipelines using tools like Python, SQL, Airflow, and Spark, while developing data ingestion strategies, ensuring data quality and governance, and collaborating with ML and AI teams to effectively integrate data into AI models.

To be a strong candidate for the Sr. Data Engineer position at our company, you should have over 5 years of experience in data engineering, demonstrating strong skills in Python and SQL. Additionally, hands-on experience with tools like Airflow, ETL pipelines, and Spark (especially Databricks) is essential. Familiarity with integrating both structured and unstructured data from various sources such as APIs and CRMs is also crucial.

The Sr. Data Engineer plays a critical role by ensuring our AI and ML models have access to high-quality, structured data. This includes developing optimal data ingestion and transformation strategies that correlate information from numerous sources, thereby powering effective AI-driven sales recommendations for enterprise users.

A Sr. Data Engineer should be well-versed in tools such as Python, SQL, Airflow, and Spark (Databricks being preferred). Additionally, experience with data ingestion strategies using APIs, web scraping, and a strong grasp of Medallion architecture for data storage is highly desirable.

At our company, the Sr. Data Engineer role offers substantial growth opportunities, especially as you take ownership of the data platform and collaborate with various teams. You can further hone your expertise in MLOps, explore other technologies like Terraform or Kubernetes, and contribute significantly to enhancing our data infrastructure in an AI-driven environment.

When answering this question, detail specific projects where you’ve designed or maintained data pipelines. Discuss the tools you've used, like Airflow or Databricks, and highlight the challenges faced and how you overcame them to ensure seamless data transformation.

Explain your approach to maintaining data quality, such as implementing strategies for data validation, cleansing processes, and governance mechanisms. Providing examples of past experiences where you successfully managed these aspects would be beneficial.

Discuss your knowledge of Medallion architecture (Bronze/Silver/Gold) and how you have applied such strategies in previous roles. Illustrate your familiarity with storage solutions that accommodate high data volumes and ensure quick access.

Share your experiences of working alongside data scientists, explaining how effective communication and understanding project objectives are essential in integrating data into ML models. Provide specific examples of collaboration that yielded positive outcomes.

Detail your proficiency in Python and SQL by discussing specific tasks you've accomplished using these languages, such as data manipulation, pipeline development, or query optimization. Highlight any advanced techniques you’ve implemented.

When responding, narrate a specific instance where you faced a significant problem in data engineering. Explain the context, your approach to finding a solution, and the outcome, focusing on the impact it had on the project.

Discuss your strategies for handling diverse types of data. Provide examples of scenarios in which you’ve successfully combined structured data from traditional sources with unstructured data from web scraping or other APIs.

Share your understanding of MLOps, elaborating on its significance in managing ML workflows, data versioning, and feature stores. You can mention any relevant experiences or projects that emphasize your insight into MLOps.

Explain your approach to staying current with the fast-evolving data engineering landscape. Mention relevant blogs, conferences, or courses you follow, as well as how you've applied new knowledge to your work.

Highlight your experience in developing data ingestion strategies by discussing specific tools or methods you've employed, such as APIs, Airbyte, or web scraping. Explain the rationale behind your choices and their effectiveness.