Senior Data Engineer

ManpowerGroup Talent Solutions

. Build data pipelines, ETL/ELT processes, and integrations using GCP services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.

Posted 10/16/2026full-timeMilwaukee • Wisconsin • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building data pipelines and ETL/ELT processes using Google Cloud Platform services, with a strong focus on data modeling, performance tuning, and machine learning integration. Proficient in translating functional requirements into technical implementations while ensuring data quality and compliance.

Highest-signal resume keywords

Data EngineeringGoogle Cloud PlatformSQLPythonMachine Learning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PipelinesETL ProcessesData ModelingPerformance TuningQuery OptimizationMachine Learning ModelsMLOpsData ValidationMetadata ManagementData Quality Checks

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

BigQueryDataflowDataprocPub/SubCloud StorageLookerVertex AIGemini Foundation ModelsDataplexIAM

Industry Keywords

Data AnalyticsAI SolutionsClient WorkshopsTechnical RequirementsCompliance

Tech Stack

Tools & technologies

BigQueryCloudETLGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Build data pipelines, ETL/ELT processes, and integrations using GCP services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.
Assist with data modeling, performance tuning, and query optimization in BigQuery.
Implement data ingestion patterns for batch and streaming data sources.
Support development of dashboards and analytics products using Looker or Looker Studio.
Assist in developing ML models and AI solutions using Vertex AI, Gemini Foundation Models, Gemini Enterprise, Model APIs, and Embeddings.
Implement ML pipelines and help establish MLOps processes for monitoring, retraining, and deployment.
Support prompt engineering, embeddings, and retrieval-augmented generation experimentation.
Contribute to model testing, validation, and documentation.
Participate in client workshops to understand data needs, use cases, and technical requirements.
Translate functional requirements into technical tasks and implementation plans.
Communicate progress, blockers, and insights to project leads and client stakeholders.
Implement metadata management, data quality checks, and lineage tracking using GCP tools such as Dataplex and IAM.
Follow best practices for security, identity management, and compliance.
Support data validation, testing, and monitoring.
Collaborate with senior data engineers, ML engineers, and architects.
Contribute to internal accelerators, documentation, and reusable components.
Stay current with GCP releases, Gemini model updates, and modern engineering practices.

Requirements

What you’ll need

Minimum of 5 years of hands-on experience in Data Engineering, Data Analytics, ML Engineering, or related fields.
Minimum of 4 years of practical experience with Google Cloud Platform.
Minimum of 5 years of experience with SQL, data modeling, and building data pipelines.
Minimum of 3 years of experience with Python or AI or GenAI tools (Vertex AI preferred).
Bachelor's degree.
Travel may be required, varying from 0 to 100% depending on business need and client requirements.