Top Senior Data Engineer Jobs in St. Louis, MO

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Design, build, and maintain scalable data pipelines and integrations across AWS, Azure, and BigQuery. Normalize, aggregate, and ensure data quality for large web and transactional datasets, leveraging APIs, ETL/orchestration tools, and big-data technologies to support analytics and BI in an e-commerce environment.

Build and maintain scalable data pipelines for healthcare claims; implement de-identification and PII-reduction; ensure data quality and observability; integrate data stores and APIs; draft technical designs and documentation; gather product feedback and iterate quickly.

Lead performance investigations across the data product stack to diagnose and fix slow queries and UX issues. Design data architecture changes, build benchmarks, measure latency/throughput/cost (ClickHouse Cloud), prototype solutions, instrument monitoring, and partner with product and engineering to balance responsiveness, scalability, and cost.

Designs and implements big-data data management solutions (Cloudera/Hortonworks/Databricks). Builds and automates reporting and analytics using SQL and Databricks, optimizes queries, manages ETL, integrates multiple data sources, enforces data governance, and supports payment integrity reporting requirements.

The Senior Data Engineer will design and implement data processing pipelines using Java and Spark, optimize performance, and mentor junior engineers while ensuring compliance with data governance standards.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.