Senior Data Engineer

As a Senior Data Engineer at Big Health, your main responsibilities will revolve around the design and construction of data pipelines that collect, process, and store data efficiently. You'll be monitoring these systems to ensure streamlined data operations, especially focusing on user behavior analytics. Your role also involves identifying automation opportunities and optimizing data flows across various platforms, collaborating with different teams to define necessary specifications that drive customer engagement.

To qualify for the Senior Data Engineer position at Big Health, you should have over 5 years of hands-on experience in data engineering, specifically with cloud data warehouses and ETL development. Proficiency in working with large datasets, and expertise in tools such as Snowflake and PostgreSQL is essential. In addition to technical skills in pySpark, SQL, and Python, understanding data security principles is also crucial for ensuring compliance with regulations.

Big Health is focused on creating a diverse and supportive workplace environment that prioritizes mental health and employee happiness. As a Senior Data Engineer here, you'll enjoy autonomy over your work, a structured development program, and generous benefits including vacation and maternity/paternity leave. The company’s commitment to a diverse team reflects in their approach to innovation and fostering a respectful work culture.

The salary for the Senior Data Engineer role at Big Health ranges from $145,000 to $190,000 per year, depending on your skills, experience, and location. This salary structure reflects the company's commitment to competitive compensation in line with market standards while providing additional benefits like bonuses and equity packages to enhance your total earnings.

Absolutely! Big Health encourages applications from candidates of diverse backgrounds. They are committed to building a culturally and experientially diverse team that values empathy and respect. This approach enriches the workplace culture and ensures that the team can effectively address a wide range of mental health needs in their mission.

When answering this question, provide specific examples of projects you've worked on that involved cloud data warehouses such as Snowflake or Redshift. Discuss the types of data you managed, the processes you implemented, and any challenges you faced along the way—this shows your hands-on experience and problem-solving skills.

Elaborate on your design process by discussing the initial steps that involve gathering requirements, defining data sources, and choosing appropriate technologies. Highlight your experience with various ETL tools and how you prioritize efficiency and scalability, ensuring the pipelines can handle large datasets and adapt to evolving needs.

Discuss your strategies based on real-world experiences. For instance, mention specific optimization techniques you’ve applied, like indexing in databases, partitioning datasets, or utilizing in-memory processing. Explain how these strategies have improved performance and reduced processing times in past projects.

Provide examples where you’ve implemented automation successfully. Describe the tools or frameworks you utilized, the processes you automated, and the impact this had on efficiency and productivity. This showcases your initiative in streamlining operations and reducing manual tasks.

Make sure to touch on both GDPR and CCPA regulations, explaining their implications for data storage and processing. Discuss how you ensure data security, confidentiality, and compliance in your work, highlighting any specific practices you have implemented to protect user data.

Share an anecdote that highlights your ability to communicate effectively with non-technical stakeholders. Emphasize how you bridge the gap between data engineering and product or business teams by translating technical jargon into understandable concepts and ensuring alignment on project goals.

As a Senior Data Engineer, it's essential to showcase your technical preferences. Discuss your proficiency in Python and SQL, as well as any frameworks like dbt or libraries such as PySpark you use regularly. Share why you prefer these tools and how they fit into your overall workflow.

Discuss your methods for identifying and troubleshooting errors within data pipelines. Explain how you implement logging for error tracking, your approach to root cause analysis, and any steps you take to prevent similar issues in the future.

When answering this question, mention specific KPIs you find crucial, such as data throughput, latency, and error rates. Explain how tracking these indicators helps maintain the health and performance of data systems, leading to improved data integrity and reliability.

Convey your enthusiasm for Big Health's innovative approach to mental health care and your alignment with their mission. Discuss how your skill set can contribute to transforming healthcare through data engineering and what you hope to achieve while working within their diverse team.