Sr. Data Engineer

Responsibilities

Join a team of innovative engineers building and scaling the core data infrastructure, pipelines, and services that power our products

Design and implement a robust data warehouse to support a wide range of analytics and operational use cases

Develop and maintain efficient data pipelines and curated data sets by working closely with stakeholders to gather requirements and translate them into technical solutions

And of course—write code every day!

Qualifications

4+ years of experience as a Data Engineer.

Proven track record of writing high-quality, production-ready Python and delivering impactful, scalable data projects.

Expertise in SQL-based data modeling and transformation frameworks (e.g. dbt), with a focus on schema management, performance, and data governance.

Strong experience with modern ingestion tools: configuring, deploying on data integration platforms (e.g. Airbyte), commercial ELT pipelines (e.g. Fivetran), and database replication methods like Change Data Capture (CDC).

Strong orchestration background: hands-on experience building robust, fault-tolerant pipelines using Apache Airflow (including custom alerting, advanced logging, and automated retry mechanisms).

Experience with modern data visualization and analytics tools such as Sigma or Tableau.

Strong knowledge of Snowflake Cloud Data Warehouse Architecture, including native ingestion patterns (e.g. Snowpipe for continuous streaming) and open table formats (e.g. Apache Iceberg).

Strong knowledge of Snowflake administration including familiarity with maintaining security compliance, role based access control (RBAC), external authentication methods, and managing downstream data consumption in external platforms.

A "QA-First" engineering mindset: Strong experience in end-to-end data quality assurance, regression testing, and data validation. Capable of troubleshooting data discrepancies, and performance-tuning complex pipelines.

Preferred Qualifications

Strong foundational knowledge of relational databases, core data warehousing principles, and dimensional modeling techniques.

Experience working with sensitive data (such as PII/PHI) within healthcare or similarly regulated environments, implementing robust data masking or redaction processes.

Familiarity with DevOps principles and infrastructure automation using Terraform.

A thoughtful approach to balancing high-quality engineering standards with tight deadlines. 

Excellent communication skills with a talent for building consensus and distilling complex technical problems into clear, actionable business priorities.