Top Data Engineer Jobs in Milwaukee, WI

Lead managed data and analytics engagements to build modern data ecosystems and convert insights into strategic opportunities. Manage client accounts and project delivery, mentor and develop staff, uphold quality standards, implement data strategies, manage risks and issues, and foster high-performing, collaborative teams.

Design and implement data architecture and scalable cloud data solutions. Build and maintain ETL/ELT pipelines, integrate and transform data, mentor junior staff, uphold technical standards, and build client relationships while understanding business context and navigating complex project scenarios.

Design and implement scalable data solutions and advanced data ecosystems, build ETL/ELT pipelines and data warehouses, analyze complex problems, mentor junior staff, maintain deliverable quality, and build client relationships.

Design, build, and maintain data infrastructure, pipelines, lakes/warehouses, and ETL processes using cloud and big-data tools. Ensure data quality, security, and performance while collaborating with clients to deliver analytics solutions and support data strategy.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, and data warehouses/lakes. Ensure data governance, security, and quality; mentor team members; partner with stakeholders to translate business requirements into scalable cloud data solutions.

Design, build, and maintain data pipelines and ETL processes, optimize SQL, create BI reports and dashboards using Oracle BI, QlikView, and Spotfire, support data architecture and modeling (star/snowflake), ensure data quality, and collaborate with clients and teams in regulated environments to deliver analytic insights.

Lead design and implementation of data architecture and pipelines, manage large-scale projects, ensure data governance and security compliance, translate business requirements into technical solutions, advise clients strategically, and coach and grow diverse high-performing teams.

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 design and delivery of data architectures and pipelines, using AWS/Redshift, DataStage/ETL, and BI tools to produce actionable insights. Coach and direct teams, engage stakeholders, apply systems thinking, validate client outcomes, and manage managed-services SLAs to optimize business performance.

Lead and manage data engineering engagements to design, build, and maintain ETL/ELT pipelines and data architectures. Deliver analytics and BI solutions using cloud platforms and tools, mentor teams, manage client relationships, ensure project delivery, and promote adoption of new technologies and best practices to drive data-driven decision-making and performance improvements.

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.

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 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.

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.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.