Data Engineer - Remote

As a Data Engineer at our company, your primary responsibilities will include designing, building, and maintaining efficient ETL/ELT pipelines to ensure data is integrated from various sources. You'll also manage PostgreSQL and Redis databases to maintain and optimize performance, collaborate with teams to provide timely and accurate data for analytics, and utilize AWS services for effective data storage solutions.

To qualify for the Data Engineer position at our company, candidates should have 0-2 years of experience in a similar role. Key skills include experience with ETL/ELT tools like Apache Airflow, proficiency in SQL for database optimization, familiarity with cloud platforms like AWS, and a solid understanding of data manipulation as well as debugging skills.

As a Data Engineer with our company, you will work with an array of tools including Apache Airflow for pipeline orchestration, Datadog and Sentry for monitoring, as well as AWS services like S3 for data storage. Familiarity with ODBC connections and integrations will also be beneficial in ensuring seamless data transactions.

Yes! The Data Engineer position is fully remote, allowing you to work from anywhere while collaborating with a diverse and dynamic team. The role requires you to follow US shift timings, which offers great flexibility for your work-life balance.

In the Data Engineer role, you will be involved in impactful projects that drive innovation in the healthcare and life sciences sectors. You will work on creating robust data pipelines, optimizing data infrastructure, and ensuring analytics teams have the timely data they need for reporting, all while using cutting-edge technologies.

In your answer, emphasize any tools you've used, such as Apache Airflow or dbt, and describe specific projects where you designed or optimized data pipelines. Illustrate your understanding of the data flow from raw sources to structured outputs.

Talk about your approach to validating and cleansing data as it flows through your pipelines, perhaps mentioning any specific tools or techniques you use to handle errors or anomalies effectively.

Provide details on the database you optimized, the performance issues you identified, and the specific changes you implemented. Highlight the improvements in performance metrics as a result of your efforts.

Share your familiarity with AWS services relevant to data engineering, such as S3 for storage or CloudFront for distribution. Include examples of how you've leveraged these services in past projects.

Discuss the tools you use for monitoring, such as Datadog or Sentry, and your strategy for setting up alerts to catch issues before they affect downstream processes. Give examples of how this has benefited your projects.

Mention your communication techniques, tools for collaboration like Jira or Slack, and provide examples of how you've successfully worked with developers and operations teams to meet shared goals.

Detail your proficiency with SQL, including any complex queries or optimizations you’ve performed. Discuss your hands-on experience in managing databases like PostgreSQL or Redis, and any best practices you adhere to.

Outline your expertise in programming languages relevant to data engineering, such as Python or Ruby, and provide examples of how you've used these skills to enhance data processing tasks.

If applicable, describe a situation where you ensured data privacy in your projects, mentioning relevant frameworks or regulations like HIPAA or GDPR, and how you implemented them in your data practices.

Discuss your methods for keeping up with industry trends, whether through online courses, reading, or community engagement. Share any recent technologies you’ve learned and how you applied them.