Top Data Engineer Jobs in St. Louis, MO

Lead and manage delivery of modern data ecosystems and managed data & analytics programs. Oversee project planning, budgeting, execution, quality, risk management, and client relationships. Mentor and develop teams, convert data insights into strategic opportunities, and uphold firm technical and professional standards.

Design and build data architecture, pipelines, and cloud data solutions; analyze complex problems, mentor junior staff, ensure governance and security compliance, and build client relationships.

Design and implement scalable data solutions and manage advanced data ecosystems. Analyze complex problems, build ETL/ELT pipelines and data warehouses, maintain data governance, mentor junior staff, and build client relationships while delivering high-quality managed data and analytics programs.

Design, build, and maintain data infrastructure, pipelines, and ETL solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, collaborate with clients, and apply data architecture, modeling, and workflow orchestration for scalable analytics.

Lead design and delivery of data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and security/compliance. Manage and mentor a team, partner with stakeholders, troubleshoot pipelines, and drive cloud-based, scalable data solutions.

Design, build, and maintain data pipelines and ETL processes; optimize SQL and databases; create BI reports and dashboards; apply data architecture and modeling (star/snowflake); support client engagements in regulated environments; use tools like DataStage, Oracle BI, QlikView, and UNIX-based scheduling; analyze data to produce strategic insights and improve data quality.

Lead design and implementation of data architecture, pipelines, and cloud-based solutions. Manage large projects, ensure data governance and security compliance, translate stakeholder requirements into technical solutions, and coach teams while providing strategic advisory to clients.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Lead development of scalable data architectures, pipelines, and BI solutions to deliver strategic insights for clients. Oversee teams in managed services engagements, apply systems thinking to solve complex data problems, validate outcomes with stakeholders, and optimize performance using advanced analytics and tooling.

Lead data engineering projects and teams to design, build, and maintain ETL/ELT pipelines, cloud-based analytics (AWS/Redshift/S3/Glue), and reporting solutions. Collaborate with stakeholders to identify opportunities, implement DataStage/GoldenGate/CDC integrations, tune performance, and mentor staff to ensure project delivery and client satisfaction while promoting new technologies and best practices.

Build, maintain, and test data pipelines and ETL/ELT processes using Python and SQL; integrate Kafka streams; model data in Snowflake; implement CI/CD (GitHub Actions), logging, observability, and on-call support. Collaborate with senior engineers and application teams to enable self-service data ingestion and improve pipeline reliability and data preview capabilities.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.