Senior Data Engineer

As a Senior Data Engineer at Alembic, you will be responsible for designing, developing, and maintaining scalable ETL pipelines, optimizing data storage solutions, and integrating analytical workloads. You'll work closely with data scientists and engineers to ensure data quality and security, while continuously evaluating new technologies to enhance our data infrastructure.

To qualify for the Senior Data Engineer position at Alembic, candidates should have over 10 years of experience in data engineering or related fields. Proficiency in SQL and Python is essential, along with a deep understanding of distributed systems, big data technologies, and cloud platforms like AWS, GCP, or Azure.

In the Senior Data Engineer role at Alembic, you'll work with various technologies, including modern data warehousing solutions like lakehouses, distributed data processing frameworks such as Apache Spark and Kafka, and cloud-based data services on platforms like AWS and Azure.

Alembic fosters an innovative and dynamic culture where every team member, including Senior Data Engineers, is encouraged to take initiative, collaborate, and contribute ideas. The fast-paced environment allows for significant personal and professional growth, ensuring that you’re not just a cog in the wheel but a vital part of a transformative journey.

At Alembic, Senior Data Engineers have ample opportunities for growth, ranging from leadership roles to specialization in targeted data technologies. The startup environment allows you to take large swings at building innovative solutions while developing your skills in a supportive and collaborative atmosphere.

When answering this question, mention common techniques such as parallel processing, incremental loading, and using efficient data formats like Parquet. Highlight your experience with specific tools and methodologies that have successfully optimized pipeline performance in your past roles.

In your response, discuss your hands-on experience with cloud platforms like AWS, GCP, or Azure. Share specific services you have utilized, such as Amazon Redshift for data warehousing or Azure Data Factory for ETL processes, emphasizing how they contributed to your projects' success.

To effectively answer this question, describe your approach to data governance practices, the implementation of validation checks, and the monitoring tools you employ to maintain data quality. Provide examples of challenges you've faced in ensuring data integrity and how you overcame them.

In your answer, mention specific big data technologies such as Apache Spark, Hadoop, and Flink. Discuss how you have integrated these technologies into your projects and the benefits they provided in handling large datasets.

Highlight your collaborative approach by discussing past experiences where you've worked closely with data scientists to integrate machine learning models or other analytical solutions into production. Illustrate your communication strategies and how you bridge the gap between data engineering and data science.

Here, explain your understanding of data modeling principles and database design for both SQL and NoSQL databases. Discuss specific projects where your designs improved performance or allowed better data access.

Emphasize your commitment to continuous learning by discussing resources you follow, such as industry blogs, conferences, or online courses. Mention specific recent trends in data engineering that excite you and how you integrate that knowledge into your work.

In your response, narrate a specific challenging project, outlining the problem, your contributions, and the outcome. Focus on the techniques you used and what you learned through the process that could benefit Alembic.

Explain how you incorporate CI/CD practices into your workflows, including automated testing, deployment, and version control systems. Highlight how these practices contribute to improved quality and efficiency in your data engineering processes.

Respond by discussing strategies such as scaling resources, using distributed processing frameworks, and optimizing storage solutions. Talk about past experiences where you successfully managed data surges and the impact on business operations.