Top Remote Data Engineer Jobs in Milwaukee, WI

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Design, build, and maintain scalable data pipelines and models; collaborate with analysts and data scientists to provision prepared datasets; implement data quality, governance and monitoring; recommend technologies; lead root-cause analysis, code reviews and testing; mentor teammates and support production data environments.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

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

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.

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.

Designs, builds, and maintains scalable ETL/ELT data pipelines and integrations. Profiles and cleanses data, creates mappings and SQL, documents metadata, monitors data quality, participates in code reviews, and collaborates with analytics, DBAs, and business partners to enable accurate analytics and data warehousing.

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.

Analyze, trace, reconcile, and migrate insurance policy data from legacy systems into a modern policy platform. Build source-to-target mappings and ETL transformations, perform data cleansing and validation, write complex SQL, use cloud databases (AWS RDS/S3), and collaborate with stakeholders using GitHub and Agile tools. Heavy daily use of AI tooling; mentorship provided throughout migration lifecycle.

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.

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.

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

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

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.

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.

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.

The Data Engineer Consultant will design and implement scalable data solutions, advise clients on optimizing data environments, and develop ETL pipelines and data models to support business objectives.

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.

In this role, you'll build and scale high-throughput data pipelines, ensure data quality, and leverage AI for improved data operations. You'll also lead initiatives in a fast-paced environment focused on real estate data engineering.