Top Data Engineer Jobs in Madison, WI

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Lead pre-sales data engineering engagements to design and implement scalable lakehouse data platforms (Snowflake/Databricks). Advise customers on data strategy, governance, pipelines, AI-ready foundations, migrations, and translate technical solutions into business outcomes while supporting sales and partner enablement.

Design, build, and maintain ingestion and transformation pipelines for a knowledge-graph analytics platform. Profile and prepare structured and semi-structured source data, ensure data lineage and traceability, implement transformation logic for graph loading, collaborate with data scientists on graph modeling and entity resolution, improve pipeline reliability, and document ingestion patterns using version control and issue tracking.

Design, build, and maintain AWS-based data pipelines and ETL processes using Glue, Spark/PySpark and EMR. Integrate multiple data sources into data lakes and Redshift, implement DevOps CI/CD practices, and work with Kafka, Hudi, and various AWS storage and analytics services.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design and build a medallion architecture using dbt and Microsoft Fabric, create and maintain data ingestion pipelines, and implement governance, lineage, documentation, and quality checks to support self-serve analytics while collaborating with cross-functional teams.

Design and implement data integration and data warehouse solutions using Big Data and open-source technologies. Build cost-efficient, performant cloud data pipelines, apply data modeling and ML concepts, and implement real-time streaming and aggregation patterns. Hands-on SQL/NoSQL and programming experience required.

Design, build, and maintain scalable data lakehouse platforms and ELT pipelines. Improve engineering practices, develop automation/tooling, monitor performance, reduce costs, and support platform modernization across Azure and cloud data technologies.

Design, build, test, and maintain scalable cloud-based data platforms and ETL/ELT pipelines (batch and near-real-time). Implement data models, data marts, governance, metadata, CI/CD and IaC, optimize pipeline performance, support migrations/DR, produce documentation, and collaborate with architects, DBAs, and stakeholders to meet federal security and audit requirements.

Build, maintain, and optimize cloud data pipelines and dbt models; design Airflow DAGs; implement dimensional models and BI dashboards; integrate healthcare data with HIPAA-compliant practices; contribute to RAG pipelines and MLOps workflows; review contractor code, monitor data quality, and document designs following governance standards.

Designs and implements big-data data management solutions (Cloudera/Hortonworks/Databricks). Builds and automates reporting and analytics using SQL and Databricks, optimizes queries, manages ETL, integrates multiple data sources, enforces data governance, and supports payment integrity reporting requirements.

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.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Designs and develops enterprise cloud data solutions, including batch, near-real-time, and API-based pipelines. Builds data models, warehouses, lakes, and lakehouses while implementing governance, quality, lineage, and curation practices. Supports CI/CD, testing, monitoring, observability, security, and compliance. Collaborates with architects, product owners, analysts, and leadership on platform strategy and modernization. Mentors engineers, leads reviews, presents technical topics, and explores AI-enabled data capabilities.

Lead Salesforce data migration and quality activities for a Department of Veterans Affairs modernization program. Analyze SQL Server data, create source-to-target mappings, execute ETL and Salesforce loading, validate and reconcile records, implement data quality controls, and develop migration and application test plans. Track defects, support automated monitoring and testing, collaborate with architects, developers, stakeholders, and provide migration status, risk, and quality updates.

Designs, builds, maintains, and optimizes scalable data pipelines and ETL/ELT workflows using Azure Databricks, SAS, SSIS, SQL, Python, Spark, and Azure services. Integrates internal and external data sources, improves pipeline performance and reliability, troubleshoots processing issues, supports Big Data solutions, and partners with engineering, product management, SRE, analytics, and cross-functional teams to deliver enterprise data platforms.

Build and own the data platform backbone: design ingestion patterns, Snowflake warehouses, governance, and freshness strategy. Implement reliable batch and streaming pipelines, data quality, observability, and onboarding processes. Lead legacy migrations, create data contracts and SLAs, and apply AI-native tooling to automate debugging, documentation, and operational workflows while partnering with Analytics, Engineering, and vendors.

Lead design and ownership of a modern data platform: ingestion, Snowflake governance, pipeline reliability, migrations, data contracts, observability, and AI-enabled tooling. Partner with Analytics, Engineering, and vendors to deliver trustworthy data products and operate production pipelines.

As a Senior Data & Analytics Engineer, you'll lead data initiatives, manage a team, and ensure data quality while turning insights into actionable decisions across the company.

Design and build Snowflake transformation layer and migrate legacy SQL to versioned dbt models. Define conformed dimensions/facts using Kimball principles, set modeling and CI/CD standards, implement data quality/lineage, support BI migration and AI/ML readiness, and mentor future hires. Hands-on work in dbt, SQL, and Python with stakeholder collaboration and governance for financial and operational data.

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

Design, build, deploy, and maintain configurable, metadata-driven analytics platforms and workflow/decision engines for Risk Adjustment. Partner with analysts to translate processes into auditable, scalable solutions; own full lifecycle, production support, monitoring, and performance optimization while applying AI where beneficial.

The Senior Data Engineer will scale and evolve the data platform, develop ETL/ELT pipelines, establish best practices, and optimize systems for performance while collaborating with various teams.

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.