CBTS

Compensation

Full Description

Position Overview 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. What You’ll Do 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. Why It Matters at CBTS Your engineering expertise forms the foundation of every AI and analytics initiative—ensuring that CBTS delivers clean, trusted, and scalable data ecosystems. By combining delivery excellence with billable accountability, you enable CBTS to meet client expectations and uphold its reputation for strategic, client-first technology transformation. Qualifications Required Preferred 3–5 years of hands-on data engineering experience with data pipeline architecture and implementation Experience with MLOps, model-serving integration, and AI-focused data infrastructure 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 Background in building reusable data delivery tools or internal accelerators Demonstrated ability to collaborate with cross-functional teams in a delivery context Experience in automated monitoring, logging, and infrastructure-as-code for pipeline environments Due to U.S. Government requirements applicable to foreign-owned telecommunications providers, non-US citizens may be required to submit to an extensive government agency background check which will necessitate disclosure of sensitive Personally Identifiable Information.