Lead Data Engineer in Madison,WI

Summary
Job Summary for Lead Data Engineer (Contract to Hire)
Location: Hybrid, Madison, WI (Local candidates only)
Duration: 6+ months contract to hire
Interview Process: 2 rounds
Key Responsibilities
Design and build robust batch and real-time data pipelines using Google Cloud Platform (GCP) technologies.
Develop scalable data ingestion frameworks for processing large volumes of structured and unstructured data.
Build and maintain cloud-native solutions supporting analytics, AI/ML, and operational reporting.
Optimize data processing performance, reliability, and cost efficiency in cloud environments.
Partner with Data Science and Analytics teams to enable advanced modeling and machine learning.
Lead the implementation of modern cloud data architectures (BigQuery, Dataflow, Dataproc, Cloud Composer, Pub/Sub, Cloud Storage, Cloud Functions).
Design data lake, data warehouse, and data product solutions for enterprise business needs.
Implement monitoring, observability, data quality, lineage, and governance best practices.
Participate in Agile processes: sprint planning, backlog refinement, and solution design.
Collaborate with distributed and offshore engineering teams.
Drive continuous improvement across development, testing, deployment, and support processes.
Required Qualifications
Bachelor s Degree in Computer Science, Engineering, Information Systems, or related field.
8-10+ years of Data Engineering experience.
3+ years in a Lead, Principal, or Senior Data Engineering role.
Expert-level Python skills for data engineering and processing.
Extensive hands-on experience with GCP (BigQuery, Dataflow, Dataproc/PySpark, Cloud Composer, Pub/Sub, Cloud Storage).
Strong SQL development and data modeling experience.
Experience building enterprise-scale ETL/ELT pipelines and optimizing large datasets.
Knowledge of CI/CD, Git, DevOps practices, and automated deployment.
Strong communication and stakeholder management abilities.
Proven ability to work independently and lead technical initiatives end-to-end.
Preferred Qualifications
Experience with Snowflake, Databricks, or lakehouse architectures.
Experience enabling AI/ML workloads and feature engineering pipelines.
Knowledge of data governance, quality frameworks, and regulatory compliance.
Experience in large enterprise Agile environments.

Work arrangement
Yes