Job Description

Role: Data Engineer (6+ Openings)

Location: Irving, TX or Hartford, CT Only (Onsite 2 days a week)

Duration: 12+ Months (LONG-TERM)

Prefers candidates on W2

Manager's Notes:

• We are seeking skilled Data Engineer(s) to support a high-impact enterprise data migration initiative .
• The goal is to migrate data warehouse assets and ETL pipelines from Teradata to Google Cloud Platform (GCP).
• The role involves hands-on development, testing, and optimization of data pipelines and warehouse structures in GCP , ensuring minimal disruption and maximum performance.

Required Skills:

• 4+ years of experience in Data Engineering , with at least 2 years in GCP.
• Strong hands-on experience in Teradata data warehousing , BTEQ , and complex SQL .
• Solid knowledge of GCP services : BigQuery, Dataflow, Cloud Storage, Pub/Sub, Composer, and Dataproc.
• Experience with ETL/ELT pipelines using custom scripting tools (Python/Java).
• Proven ability to refactor and translate legacy logic from Teradata to GCP .
• Familiarity with CI/CD, GIT, Argo CD, and DevOps practices in cloud data environments.
• Intangibles : Strong analytical, troubleshooting, and communication skills. Problem solving mindset, Attention to detail, Accountability and ownership

Preferred Qualifications:

• GCP certification (Preferred: Professional Data Engineer).
• Exposure to Apache Kafka, Cloud Functions, or AI/ML pipelines on GCP.
• Experience working in the healthcare domain.
• Knowledge of data governance, security, and compliance in cloud ecosystems.

Key Responsibilities:

• Execute migration of data and ETL workflows from Teradata to GCP-based services such as BigQuery, Cloud Storage, Dataflow, Dataproc, and Composer (Airflow).
• Analyze and map existing Teradata workloads to appropriate GCP equivalents.
• Rewrite SQL logic, scripts, and procedures in GCP-compliant formats (e.g., standard SQL for BigQuery).
• Collaborate with data architects and business stakeholders to define migration strategies, validate data quality, and ensure compliance.
• Develop automated workflows for data movement and transformation using GCP-native tools and/or custom scripts (Python).
• Optimize data storage, query performance, and costs in the cloud environment.
• Implement monitoring, logging, and alerting for all migration pipelines and production workloads.

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