Lead Data Engineer

KINGS AUTO SALES

Cincinnati, OH, United States

29 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

2 years minimum

Compensation

$92,300.0 - $166,850.0

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Microsoft Azure Big Data Software Quality Code Review Data Validation Information Engineering Data Infrastructure Extract Transform Load (ETL) Data Transformation Data Warehousing Python (Programming Language) Performance Tuning

+9 more

SQL Databases Data Processing Netezza Apache Spark Pyspark Ibm Netezza Data Lakehouse Data Pipelines Databricks

Job description

Lead onshore and offshore data engineering teams across mixed vendor groups, including Capgemini and other partners.
Drive technical delivery for migration from Netezza to Databricks.
Translate business and architecture requirements into technical designs and engineering tasks.
Develop and optimize ETL/ELT pipelines, data transformations, and Databricks workflows.
Guide conversion of legacy Netezza SQL, scripts, and data processing logic into Databricks solutions.
Ensure code quality, performance tuning, testing, and adherence to engineering best practices.
Coordinate with architects, project managers, business stakeholders, and vendor teams.
Provide mentoring and technical direction to junior and mid-level data engineers.
Participate in code reviews, issue resolution, deployment, and production support.
Support data validation, reconciliation, cutover planning, and post-migration stabilization.

Requirements

8+ years of experience in data engineering, data warehousing, ETL/ELT, or analytics engineering.
2+ years of experience leading onshore/offshore engineering teams.
Strong hands-on expertise with Databricks, Apache Spark, SQL, and Python/PySpark.
Experience with IBM Netezza or similar legacy data warehouse platforms.
Experience supporting data platform migration or modernization initiatives.
Familiarity with GCP and Azure cloud platforms.
Strong understanding of data pipelines, data lakehouse concepts, and large-scale data processing.
Experience working with global delivery teams and multiple vendor partners.
Strong communication, problem-solving, and technical troubleshooting skills.