Lead Data Engineer

ONLY W2, NO C2C
Lead Data Engineer
Hybrid Madison, WI (LOCALS ONLY)
6+ Months CONTRACT TO HIRE
INTERVIEW TYPE 2 Rounds

Data Engineering & Platform Development
Design and build robust batch and real-time data pipelines using Google Cloud Platform technologies.
Develop scalable data ingestion frameworks capable of processing large volumes of structured and unstructured data.
Build and maintain cloud-native solutions that support analytics, AI/ML, and operational reporting.
Optimize data processing performance, reliability, and cost efficiency across cloud environments.
Partner with Data Science and Analytics teams to enable advanced modeling and machine learning initiatives.

Cloud & Data Architecture
Lead the implementation of modern cloud data architectures utilizing:
BigQuery
Dataflow
Dataproc
Cloud Composer
Pub/Sub
Cloud Storage
Cloud Functions
Design data lake, data warehouse, and data product solutions that support enterprise business needs.
Implement monitoring, observability, data quality, lineage, and governance best practices.

Agile Delivery
Participate in sprint planning, backlog refinement, and solution design sessions.
Collaborate with distributed teams, including offshore engineering resources.
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 capacity.
Expert-level proficiency with Python for data engineering and data processing.
Strong hands-on experience with Google Cloud Platform (Google Cloud Platform).
Extensive experience with:
BigQuery
Dataflow
Dataproc / PySpark
Cloud Composer (Airflow)
Pub/Sub
Cloud Storage
Strong SQL development and data modeling experience.
Experience building enterprise-scale ETL/ELT pipelines.
Experience working with large-volume datasets and performance optimization.
Knowledge of CI/CD, Git, DevOps practices, and automated deployment frameworks.
Strong communication and stakeholder management skills.
Ability to work independently and lead technical initiatives from concept through production.

Preferred Qualifications
Experience with Snowflake, Databricks, or modern lakehouse architectures.
Experience enabling AI/ML workloads and feature engineering pipelines.
Knowledge of data governance, data quality frameworks, and regulatory compliance requirements.
Experience working within large enterprise Agile environments.