Senior Data Engineer (Databricks | Data & AI)

RADcube - A NLogix Company•Indianapolis, IN

1d•Onsite

About The Position

We are seeking a senior hands-on Data Engineer to design, develop, and scale enterprise data platforms supporting data and AI initiatives for a client. The role supports one of the client’s business units, with potential for the engagement to expand. The ideal candidate has deep experience building on AWS and Databricks, can work directly with business and technical stakeholders, understands emerging requirements, and identifies opportunities for additional data and AI solutions. The position is based in Indianapolis, Indiana, and requires onsite work.

Requirements

Strong hands-on data engineering background.
Experience with Python, PySpark/Spark, and SQL.
Experience with Databricks, including Delta Lake and Lakehouse.
Strong hands-on experience with AWS data services.
Experience developing ETL/ELT pipelines and data modelling.
Experience with batch and streaming ingestion, data quality and governance, and CI/CD.
Ability to engage stakeholders independently, not only work from technical tickets.
Excellent communication and stakeholder management skills.
Must be local to Indianapolis, Indiana, and able to work onsite.

Nice To Haves

Experience with Unity Catalog, MLflow, and Medallion Architecture.
Databricks certification.
Azure experience.
NVIDIA ecosystem exposure.
Experience with GenAI and ML pipelines.
Regulated healthcare or pharma experience; pharma is preferred but not mandatory.
Experience identifying additional technology or business opportunities.

Responsibilities

Design, build, and scale data pipelines on Databricks and AWS.
Develop ETL/ELT solutions using Spark, PySpark, and SQL.
Implement Delta Lake, Lakehouse, Unity Catalog, and Databricks Workflows.
Work with AWS data services such as S3, Glue, Lambda, Redshift, and IAM.
Build batch and streaming ingestion with data quality and governance controls.
Apply CI/CD practices and optimize pipeline performance, cost, and reliability.
Engage business and technical stakeholders directly and translate requirements into solutions.
Contribute to data architecture and solution design discussions.
Identify opportunities where data and AI can address additional business needs.
Contribute to the growth of the overall engagement.