Top Data Engineer Jobs in St. Louis, MO

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.

Provides hands-on technical leadership for foundational data engineering and analytics engineering. Owns multi-year data architecture, production pipelines, dimensional models, and trusted metrics spanning cloud costs, traffic, infrastructure, and AI usage. Designs new datasets integrating cost, utilization, performance, and reliability signals; establishes standards, coaches engineers, aligns stakeholders, and drives adoption across Infrastructure, Finance, Data Science, and Product Engineering.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Own and evolve production data systems supporting healthcare, claims, billing, analytics, and reporting. Design resilient ingestion patterns, data contracts, models, schemas, and pipelines that handle incomplete, duplicated, delayed, and changing data. Improve performance, observability, failure handling, retries, backfills, and replay safety. Collaborate with engineering, product, operations, and analytics teams while making architecture decisions and documenting technical changes.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Leads the architecture, development, and support of enterprise data warehouses, lakehouses, dimensional models, ETL/ELT pipelines, and governed data products using Microsoft Fabric and related tools. Ensures data quality, security, scalability, performance, and reliability for analytics and reporting. Partners with technical and business stakeholders, documents data solutions, guides architecture decisions, mentors team members, and drives continuous improvement of data engineering standards and practices.

Designs, builds, tests, maintains, and optimizes large-scale data pipelines, ETL processes, ingestion workflows, and cloud data storage. Develops Databricks data engineering and AI/ML pipelines, Azure Data Lake solutions, Power BI dashboards, and CI/CD automation. Ensures data quality, security, scalability, cost efficiency, and reusable engineering practices while collaborating with distributed technical teams.

Designs and maintains scalable ETL pipelines, data models, integrations, cloud databases, and healthcare data infrastructure. Ensures security, HIPAA/HITRUST compliance, reliability, monitoring, and documentation while collaborating with product, engineering, and clinical teams. Requires strong SQL and Python expertise, modern data warehousing and orchestration experience, cloud platform knowledge, and familiarity with healthcare interoperability and claims data.

Build and scale Benepass’s data platform, including warehouses, replication pipelines, orchestration, dbt transformations, Cube semantic models, and governed analytics infrastructure. Own data quality, security, compliance, reliability, performance, cost optimization, and documentation. Partner with Product, Engineering, Operations, GTM, Finance, analysts, and data scientists to deliver trusted reporting and data solutions. Shape the platform roadmap, mentor colleagues, and drive architectural improvements as a senior individual contributor.

Build and scale Benepass’s data platform, including warehouses, replication pipelines, orchestration, dbt transformations, semantic models, and data quality systems. Own platform reliability, governance, security, performance, cost optimization, and developer experience. Partner with Product, Engineering, Operations, GTM, Finance, analysts, and data scientists to deliver trusted reporting and analytics infrastructure. Shape the data platform roadmap, evaluate new technologies, and mentor cross-functional partners as a senior individual contributor.

Lead Data Engineer responsible for designing, maintaining, and improving data pipelines and platform infrastructure across ingestion, streaming, transformation, and delivery. The role uses Airflow, Spark, Iceberg, Kafka, Flink, dbt, Terraform, and GCP. Responsibilities also include defining data contracts, documenting systems, optimizing performance, reviewing code, supporting access controls and compliance, and collaborating with stakeholders and teammates.

Leads a data engineering team and supervises one Data Engineer while designing, maintaining, and optimizing reliable data pipelines and infrastructure. The role integrates internal and external data sources, supports SaaS analytics dashboards and connected platforms, improves data-store performance, and ensures infrastructure quality, security, and uptime. Requires proficiency in Python, databases, distributed processing, orchestration, AWS, and machine learning.

Maintain and administer Microsoft SQL Server databases supporting healthcare research administration, compliance, reporting, and operations. Design schemas and tables; develop and optimize stored procedures, queries, scripts, utilities, and reports; manage scheduled jobs; monitor performance, security, backups, recovery, and data integrity; troubleshoot database and application issues; coordinate with web developers; document solutions; and provide technical recommendations to research leaders.

Leads data engineering teams in designing, developing, and supporting scalable batch and streaming data solutions. Responsibilities include data pipeline development, architecture, governance, security, platform optimization, production support, technical mentorship, stakeholder collaboration, and delivery leadership. The role requires hands-on engineering with Python or similar languages, modern data technologies, cloud platforms, DevOps practices, and people-management responsibilities.

Supports the design and development of scalable end-to-end data pipelines and cloud data platforms. Responsibilities include improving data reliability and quality, implementing data management practices, supporting automation and process improvements, ensuring secure and cost-effective solutions, and recommending system enhancements. The intern collaborates with global teams and gains hands-on experience in data engineering, project coordination, and innovation management.

The Principal Data Engineer designs and builds scalable cloud-native data platforms, pipelines, frameworks, and governed data products supporting analytics, reporting, AI, and operational workloads. Responsibilities include Databricks, Spark, Delta Lake, Azure, SQL Server, batch and streaming processing, data integration, performance optimization, governance, modernization, troubleshooting, architecture reviews, and mentorship. The role provides senior technical leadership without direct people management and supports insurance-domain data across policy, claims, billing, finance, and regulatory functions.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Lead production of federal species range maps for marine and anadromous taxa. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersects; coordinate iterative reviews with FWS and NOAA SMEs; prepare QA documentation; and secure government sign-off for map deliverables.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.