Data Engineer Level 2

Experis

Cincinnati (OH)

On-site

USD 96,000 - 103,000

Full time

4 days ago

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Job summary

Experis is seeking a Senior Data Engineer to design, build, and optimize scalable data solutions across Azure and GCP cloud environments. This onsite role in Cincinnati focuses on Python, Vertex AI, feature engineering, and production-grade pipelines.

You will own data pipelines through the SDLC, enabling ML workflows and scalable data platforms for real-time analytics, collaborating with cross-functional teams to improve testing, performance, and cost efficiency.

Qualifications

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.

Responsibilities

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 ML workflows using Vertex AI, BigQuery ML, and Python.
Work with SQL, NoSQL, cloud-based systems, 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 support data quality and reliability.
Optimize data workflows for performance, scalability, and cost efficiency across large datasets.
Own 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.

Skills

Python

SQL

NoSQL

Tools

Databricks

Kafka

BigQuery

BigQuery ML

Vertex AI

Azure

Job description

Experis is hiring a Senior Data Engineer to design, build, and optimize scalable data solutions across Azure and GCP cloud environments. This onsite role in Cincinnati supports modern data engineering efforts, with a focus on Python, Vertex AI, feature engineering, and production-grade pipeline reliability.

Role Focus

You will work across the full SDLC, taking ownership from design through deployment and support. The work includes building and improving data pipelines for ingestion, transformation, and integration, as well as enabling machine learning feature engineering workflows that connect to Vertex AI and BigQuery ML.

Responsibilities

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 ML workflows using Vertex AI, BigQuery ML, and Python.
Work with SQL, NoSQL, cloud-based systems, 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 support data quality and reliability.
Optimize data workflows for performance, scalability, and cost efficiency across large datasets.
Own 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.

Requirements

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.