Senior Data Engineer

Overview

Remote
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 6 Month(s)

Skills

BigQuery
GCP
SSIS

Job Details

Responsibilities:

  • Lead the migration of the existing SSIS-based ETL workflows to cloud-native pipelines using DBT and/or Google Cloud Platform tools such as Dataflow, Dataform, or Cloud Composer (Airflow).
  • Design and implement scalable, efficient data models in BigQuery, following best practices for dimensional modeling.
  • Optimize and maintain existing SQL transformations, ensuring correctness and performance in the cloud.
  • Collaborate with BI developers and analysts to ensure data marts align with Tableau reporting needs.
  • Ensure data integrity, security, and lineage through testing, documentation, and observability practices.
  • Work with on-prem teams to phase out legacy systems and design transitional architectures where needed.
  • Establish best practices and mentor junior engineers on cloud-first data engineering patterns.

Qualifications:

Required:

  • 5+ years of experience in data engineering with strong SQL and ETL skills.
  • Experience with SSIS and legacy SQL Server based data marts.
  • Proficiency in Google BigQuery and/or similar cloud data warehouses.
  • Hands-on experience with DBT or modern transformation frameworks.
  • Strong knowledge of dimensional modeling and data warehousing principles.
  • Experience migrating on-prem pipelines to cloud platforms.
  • Familiarity with Google Cloud Platform-native services such as Cloud Storage, Pub/Sub, Dataflow, Composer, and IAM.
  • Strong knowledge of Healthcare Information Systems.

Preferred:

  • Experience supporting or integrating with Tableau-based BI solutions.
  • Exposure to infrastructure-as-code tools like Terraform for Google Cloud Platform.
  • Knowledge of data observability tools and practices.
  • Comfortable with Git-based CI/CD for data pipeline deployments.

Nice to Have:

  • Familiarity with Google Cloud Platform networking and cost optimization strategies.
  • Experience with data validation or automated testing frameworks for pipelines.
  • Knowledge of metadata management or cataloging tools (e.g., Data Catalog, DataPlex).

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