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# Data Engineer Level 2

- **Company:** Manpower
- **Location:** Cincinnati, OH, United States
- **Experience:** Expert
- **Salary:** $145,600.0 - $156,000.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Automation of Tests, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Information Engineering, Extract Transform Load (ETL), Data Systems, Distributed Computing Environment, Python (Programming Language), NoSQL, Systems Development Life Cycle, Standard Sql, SQL Databases, Data Streaming, Google Cloud, Cloud Platform System, Performance Testing, Feature Engineering, Apache Kafka, Data Management, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** September 21, 2026
- **Apply:** https://dejobs.org/x/x/409CF91731A74C5CB5124BDBD60B74B4/job/

## About the Role

* Strong Python development experience.
 * Hands-on experience with Google Cloud Platform (GCP) and Azure.
 * Strong experience with Vertex AI and feature engineering.
 * Experience with Databricks, Kafka, and BigQuery/BigQuery ML.
 * Strong understanding of data pipelines, ETL/ELT, streaming, and distributed data processing.
 * Experience implementing automated testing for data applications and pipelines.
 * Strong SQL and experience working with large-scale datasets.
 * Excellent problem-solving, communication, and cross-functional collaboration skills.

## Description

We are seeking a Senior Data Engineer to design, build, and optimize scalable data solutions across Azure and GCP cloud environments. The ideal candidate will have strong expertise in Python, Vertex AI, feature engineering, and modern data engineering technologies., * Design, build, and maintain scalable data pipelines for ingestion, transformation, and integration using Kafka, Databricks, and related technologies.

 * Develop and manage feature engineering pipelines for machine learning workflows using Vertex AI, BigQuery ML, and Python.
 * Work with SQL, NoSQL, cloud-based, and real-time streaming data platforms.
 * Drive modernization and innovation across data platforms and engineering processes.
 * Develop automated unit, integration, and performance testing frameworks to ensure data quality and reliability.
 * Optimize data workflows for performance, scalability, and cost efficiency across large datasets.
 * Take ownership of systems and processes throughout the full SDLC, from design through deployment and support.
 * Collaborate with cross-functional teams to deliver internal and external-facing data solutions.
 * Create and review architecture diagrams, interface specifications, and technical documentation.