Top Remote Data Engineer Jobs in St. Louis, MO

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and operate cloud data capabilities, including ingestion pipelines, transformations, data models, curated data products, and data-quality controls. Use Snowflake, dbt, SQL, and Python to deliver governed data for analytics, applications, automation, and AI workflows. Implement testing, CI/CD, documentation, lineage, access controls, monitoring, incident resolution, and performance optimization. Collaborate with engineering, analytics, governance, security, and business teams in an Agile, product-oriented environment with active AI-assisted development.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Design and maintain scalable AWS-based data pipelines, architectures, ETL/ELT workflows, databases, and big data solutions. Integrate and transform complex datasets using Python, SQL, Spark, Hadoop, and Kafka. Build data models, dashboards, reports, and analytics solutions while implementing data quality, governance, security, monitoring, and compliance processes. Support migrations and modernization projects, collaborating with data scientists, analysts, and business stakeholders to deliver reliable, decision-ready data.

Design and modify data solutions using Databricks and Palantir Foundry; build data models and reports from ERP and legacy-system datasets; define and execute tests; and translate stakeholder reporting needs into technical specifications. The role requires SQL, Python, PySpark, database architecture, cloud architecture, software engineering practices, independent execution, documentation, and collaboration. It supports an Army client in a hybrid Arlington, Virginia environment and requires an active Secret clearance.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

Designs, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.

Architects, builds, tests, and maintains scalable data infrastructure and ELT/ETL pipelines using AWS Glue, Glue Catalog, and Step Functions. Performs data modeling, SQL querying, data migration, schema optimization, validation, and data quality improvements. Partners with analytics and business teams to transform raw data into production-ready datasets. The role may also support AI/ML pipelines, governance, access controls, DevOps workflows, and real-time data architectures.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Designs, builds, operates, and modernizes secure ETL/ELT data pipelines for public health interoperability. Responsibilities include using NiFi, Kafka, Python, and SQL; deploying pipelines on Azure or AWS; implementing CI/CD with GitHub Actions; monitoring with Grafana; troubleshooting production systems; supporting legacy migrations; and ensuring data security, compliance, scalability, and reliability. The role collaborates with architects, integration engineers, data scientists, DevOps/SRE, and product teams.

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.

Build and maintain production ETL pipelines for structured and unstructured data, integrate machine learning models for enrichment, develop data transformations and quality monitoring, design warehouses and dimensional models, establish governance frameworks, and contribute to data architecture and platform improvements. Collaborate with data scientists, analysts, and engineering teams to deliver reliable, actionable intelligence.

Designs and optimizes Power BI dashboards, semantic models, datasets, DAX measures, and reporting solutions for identity governance and administration analytics. Partners with ETL engineers and stakeholders to validate Parquet data from Azure Synapse and SailPoint sources, establish data-quality checks, monitor refreshes, define KPIs, and document data lineage. Supports compliance reporting, access governance, audit readiness, certification metrics, and Zero Trust analytics.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop data warehouse models, transformations, automation, quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Build and maintain scalable batch and incremental data pipelines using Python, SQL, and Snowflake. Develop warehouse models, transformations, automation, data quality checks, lineage, alerting, and performance optimizations. Collaborate with analysts, data scientists, and application teams to deliver trusted datasets, while documenting pipelines, schemas, and data contracts.

Designs and maintains a scalable data platform supporting analytics, machine learning, and AI workloads. Builds tooling for reliable data pipelines, semantic layers, data agents, LLM applications, and evaluation systems. Ensures data quality, availability, observability, and reliability while establishing modeling, testing, and platform best practices. Collaborates with Data Science and Product Engineering, leads solutions from design through implementation, and contributes individually in a fast-paced startup environment.

Build, test, and maintain batch and event-driven data pipelines and ETL/ELT workflows. Transform and validate large datasets, troubleshoot data-quality issues, improve workflow reliability, and support platform modernization. Collaborate with engineering, product, and data stakeholders while following strong practices for version control, testing, monitoring, documentation, and code review. The role may occasionally require work outside standard business hours.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

Build and maintain scalable batch and streaming data pipelines, analytics warehouses, transformation layers, and dimensional data models. Perform SQL-based analysis, support dashboards and reporting, monitor data quality and delivery SLAs, troubleshoot incidents, and improve operational reliability. Collaborate with engineers, analysts, data scientists, and business stakeholders while contributing to documentation, governance, observability, and automation.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and data quality processes. Design scalable processing systems, orchestration workflows, storage patterns, and data warehouse solutions. Collaborate remotely, document technical designs, scope projects, and solve complex data engineering and data science problems.

Build and scale distributed data pipelines and architectures supporting AI products and research. Responsibilities include processing large datasets with Spark, designing AWS data systems, managing SQL and NoSQL storage, writing Python and SQL, ensuring data quality and reliability, and implementing automation, orchestration, monitoring, and scalable data operations.