Data Engineer

Our client is seeking a Data Engineer to design, develop, and optimize scalable data pipelines and ETL/ELT workflows using GCP, BigQuery, Apache Spark, and Dataproc. This role will build and support large-scale batch and streaming data solutions while improving performance, reliability, data quality, and cost efficiency across Target’s data platforms. The ideal candidate has strong SQL, Python and/or Java/Scala, distributed data processing, DevOps/CI/CD, and production troubleshooting experience.

Work Details

• Location: Hybrid - Brooklyn Park, MN 55445
• Duration: 10/12/2026 - 4/9/2027
• Contract Length: 6 month contract
• Hours: 40/week
• Experience: 1-8 years

Required Skills & Experience

• Strong hands-on Data Engineering experience building production-grade data pipelines
• Strong ETL/ELT development experience
• Hands-on Google Cloud Platform (GCP) experience
• Strong BigQuery skills, including SQL development and query optimization
• Hands-on Apache Spark and distributed data processing experience
• Experience with GCP Dataproc
• Experience with Hadoop and/or Apache Kafka
• Strong SQL skills working with large datasets
• Proficiency in Python and/or Java/Scala
• Understanding of data modeling, partitioning, distributed processing, and common data/file formats
• Experience with DevOps/CI/CD, Git, automated deployments, and production support
• Strong troubleshooting and problem-solving skills

Nice to Have

• Looker and/or LookML
• GCS, BigLake, Hive, Apache Iceberg, and Parquet
• Terraform or other Infrastructure-as-Code tools
• Kafka-based streaming pipeline experience
• Experience migrating on-premises Hadoop/Hive workloads to GCP
• Large-scale data lake or data platform modernization experience
• Familiarity with data quality, governance, lineage, and metadata management
• Power BI

Day-to-Day Responsibilities

• Design, develop, test, and maintain scalable data pipelines and ETL/ELT workflows
• Build distributed processing solutions with Apache Spark and GCP Dataproc
• Develop and maintain analytical datasets and workloads in BigQuery
• Build batch and/or streaming solutions using Spark, Hadoop, and Kafka
• Develop ingestion and transformation pipelines across source systems, data lakes, and analytical platforms
• Optimize Spark and BigQuery workloads for performance, scalability, reliability, and cost
• Troubleshoot production pipelines, identify root causes, and implement durable fixes
• Build data-quality checks, monitoring, logging, alerting, and operational controls
• Apply software engineering and DevOps practices, including testing, source control, CI/CD, and deployment automation
• Partner with data architects, platform engineers, application teams, and business stakeholders
• Participate in code reviews and develop reusable engineering frameworks and best practices