Top Data Engineer Jobs in Milwaukee, WI

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Lead enterprise data platform strategy and architecture, define data standards and governance, drive Snowflake maturity, oversee legacy data migrations, design ingestion and DataOps patterns, and enable analytics, reporting, AI, and reusable data products across the organization.

Design, build, and maintain scalable ETL/ELT data pipelines and data models integrating USMC logistics systems (GCSSMC, TLS). Ensure data quality, RMF/cybersecurity compliance, support analytics, dashboards, APIs, and collaborate with NIWC Atlantic, stakeholders, and cross-functional teams to improve logistics visibility and readiness.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.

Build and maintain AWS-based ETL pipelines (Glue/PySpark, MWAA/Airflow, Lambda) to ingest and process terabytes of financial data into an S3 data lake using Iceberg/Parquet. Implement metadata, monitoring, tests (90% coverage), materialized views (Trino/Athena), CloudFormation deployments, documentation, and support agile delivery and operations for federal clients.

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.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

Develop custom connectors on Airbyte for data ingestion from various platforms, ensuring accurate data integration and transformation. Collaborate with stakeholders and perform testing for reliability.

The Snowflake Data Engineer will work on data cleansing and harmonization, moving from legacy systems to SAP S4, focusing on ETL/ELT development and view creation.

This role involves designing, building, and maintaining data pipelines on Snowflake. Responsibilities include ensuring data quality and collaborating with stakeholders to deliver solutions.

The AWS Data Engineer will work on a data cleansing and harmonization project, focusing on AWS ETL design and development using various AWS services.

The Snowflake Data Engineer will design, build, and maintain data pipelines on Snowflake, optimizing performance and ensuring data quality while collaborating with stakeholders.

The Snowflake Data Engineer and Product Owner will manage the Snowflake data analytics project, collaborating with stakeholders, leading Agile practices, and ensuring quality deliverables through effective team coordination and backlog management.

The Data Engineer will develop and maintain data pipelines, perform data extraction and transformation, and support data analytics across the enterprise.

As a Data Engineer, you will develop solutions for customer-centric technology, ensuring platform integration and managing data architecture. Responsibilities include building data processes, maintaining data integrity, and collaborating cross-functionally to meet business challenges.

The Principal Data Engineer designs and maintains scalable data systems, evaluates new technologies, mentors junior engineers, and leads data engineering initiatives in agile environments.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Design, build, and maintain scalable ETL/ELT pipelines and Lakehouse solutions; ingest and transform diverse data formats; optimize Databricks and SQL Server environments; implement data quality, lineage, and governance; support batch and streaming ingestion; collaborate with analysts and stakeholders to enable analytics (fraud/anomaly detection, financial oversight); troubleshoot and improve pipelines. Candidates must be willing to undergo a U.S. government background investigation.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

Design, build, and migrate large-scale batch data pipelines into Databricks. Develop and optimize production-grade Python/Spark/SQL solutions, support parallel legacy and cloud systems during migration, lead technical design discussions, use AI tools to improve workflows, ensure pipeline reliability through testing/monitoring/tuning, and participate in a rotating on-call schedule.

No responsibilities or qualifications were provided. Title indicates a senior data engineering role typically responsible for designing, building, and maintaining data pipelines and data infrastructure.