Data Engineer

CBTS-posted 3 months ago
1,001-5,000 employees
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As a client-facing Data Engineer at CBTS, you are the architect behind the data infrastructure that enables AI-powered business transformation. You will be responsible for designing, building, and maintaining scalable and secure data pipelines that directly support client delivery. At the same time, your work also contributes to CBTS’s financial performance through billable implementations.

  • Develop and Optimize Data Pipelines
  • Architect and build robust ETL/ELT pipelines to ingest, process, and transform high-volume, high-variety data, ensuring reliability and performance at each stage.
  • Ensure Data Integrity and Governance
  • Implement data validation, lineage, access controls, and security protocols to maintain data quality and compliance across all touchpoints.
  • Enable Production Readiness
  • Collaborate with Data Scientists and ML Engineers to design pipelines and storage solutions that reliably feed model training and deployment in production environments.
  • Drive Scalable Platform Enhancements
  • Build and maintain reusable, modular components and accelerators that improve the efficiency and repeatability of data delivery across client engagements.
  • Track Utilization and Client Value
  • Maintain accurate time tracking against billable projects, ensuring transparency in deliverables, utilization, and the value delivered to clients.
  • 3–5 years of hands-on data engineering experience with data pipeline architecture and implementation
  • Proficiency in SQL, Python, and modern ETL/ELT frameworks (e.g., Spark, Airflow)
  • Familiarity with cloud data platforms (e.g., AWS Glue/Redshift, Azure Synapse, Google BigQuery)
  • Strong understanding of data governance, security, and performance optimization
  • Demonstrated ability to collaborate with cross-functional teams in a delivery context
  • Experience with MLOps, model-serving integration, and AI-focused data infrastructure
  • Background in building reusable data delivery tools or internal accelerators
  • Experience in automated monitoring, logging, and infrastructure-as-code for pipeline environments
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