Senior Data Engineer: Databricks Lakehouse & Pipelines

Steampunk; Bloomington (IL); On-site; USD 140,000 - 180,000; Full time

Job summary

Steampunk seeks a seasoned Senior Data Engineer to design and build enterprise-grade data platforms, data lakes and lakehouses, and robust pipelines using Databricks. You will collaborate with teams and clients to deliver scalable data solutions across AWS, Azure, and GCP, focusing on performance, reliability, and governance.

You should have 5–7 years of data engineering experience, strong SQL and PySpark, and familiarity with Databricks Workflows, CI/CD, and data cataloging tools. U.S.

Qualifications

• Bachelor's degree in a technical field required.
• 5–7 years of production software experience.
• 5–7 years in Data Engineering with Databricks/Spark/Delta Lake.
• Strong SQL knowledge and database experience.
• Experience with data pipelines/workflows (Airflow, Databricks Workflows).
• Cloud experience across AWS/Azure/GCP.

Responsibilities

• Lead design and migration of data lake and lakehouse environments using Databricks.
• Assess and optimize ETL jobs, data marts, BI tools, and data dependencies.
• Build scalable data pipelines in cloud environments (AWS/Azure/GCP).
• Apply Databricks, SQL, PySpark/Python and cloud tech to data solutions.
• Support Agile SDLC and data governance practices.
• Contribute to AI & Data Exploitation Practice growth.

Skills

Databricks

SQL

PySpark/Python

Cloud technologies

Agile

Communication

Education

Bachelor's degree in computer science, information systems, engineering, or related technical discipline

Tools

Databricks

Apache Spark

Delta Lake

Airflow

AWS Step Functions

S3 / EC2 / RDS

Job description

Steampunk seeks a seasoned Senior Data Engineer to design and build enterprise-grade data platforms, data lakes and lakehouses, and robust pipelines using Databricks. You will collaborate with teams and clients to deliver scalable data solutions across AWS, Azure, and GCP, focusing on performance, reliability, and governance.

You should have 5–7 years of data engineering experience, strong SQL and PySpark, and familiarity with Databricks Workflows, CI/CD, and data cataloging tools. U.S.