Senior Data Engineer, Thirty Madison

• 5+ years of experience experience in Data Engineering, Data Science, Analytics Engineering, or similar field
• Expertise in SQL and proficient in at least one data engineering language, such as Python or Scala
• Experience with Looker and data visualization tools generally
• Excellent communication skills with a history of strong collaborations with technical and non-technical partners
• Experience with an ETL framework (e.g., Prefect, Airflow, or Dagster )
• Expertise with data warehousing and modeling. Hands-on experience with relational databases) and columnar databases (e.g., Snowflake, BigQuery, Redshift)
• Experience with the modern data technologies (Fivetran, Snowflake, dbt, Looker, Hightouch, Hex, or equivalents)
• Strong analytical and problem-solving abilities, with the capability to simplify complex issues into actionable plans
• Ability to mentor and guide other data engineers
• Comfortability working in ambiguous environments where problems are not well-defined and evolve quickly

Desirable

• As a Senior Data Engineer, you will be working alongside an existing team of experienced Data engineers, Analytics engineers, Machine Learning Engineers, and Analysts partnering closely with technologists across the company to help build the future of digital health security and protect our patients
• We serve our patients from end to end, and data works the same way, all the way from the deepest infrastructure to the patient experience, we want our patients data to be safe and secure
• Above all, you embody the Thirty Madison mission of providing access to healthcare for all who suffer from chronic conditions
• Design and develop robust data architectures and data pipelines to support data ingestion, processing, storage, and retrieval. Evaluate and select appropriate technologies, frameworks, and tools to build scalable and reliable data infrastructure
• Optimize data engineering systems and processes to handle large-scale data sets efficiently. Design solutions that can scale horizontally and vertically
• Collaborate with cross-functional teams, such as data scientists, software engineers, and business stakeholders, to understand data requirements and deliver solutions that meet business needs. Effectively communicate complex technical concepts to non-technical stakeholders
• Develop audits for data quality at scale, implementing alerting and anomaly detection as necessary
• Model the business data layer (normalized and denormalized) that will expose data to downstream consumers, making our data more actionable and clean
• Create scalable dashboards and reports to support business objectives and enable data-driven decision making