Senior Data Engineer

About This Role

Lead the design and deployment of sophisticated data architectures for production-grade AI and ML applications for clients as a Senior Data Engineer. This role focuses on setting standards for data quality, modeling, and orchestration, enabling the transition from AI prototypes to resilient enterprise-scale systems.

Responsibilities

• Architect and build scalable, high-volume ELT/ETL pipelines that ingest complex data sets from diverse client systems into centralized cloud warehouses.
• Lead the audit and optimization of existing data workflows, refactoring inefficient queries and data models to significantly reduce latency and operational costs.
• Design and implement comprehensive data quality frameworks, ensuring automated testing and validation logic catches anomalies.
• Design and implement data pipelines specifically for AI workloads, including the management of vector databases to support RAG and model inference.
• Own the roadmap for data platform stability and scalability enhancements.
• Provide technical mentorship to the engineering team, conducting rigorous code reviews and driving improvements in coding standards, documentation, and system reliability.
• Standardize the organization’s approach to the Modern Data Stack (MDS), driving the adoption of best practices across diverse client engagements.

Requirements

• 6+ years of data engineering experience with a focus on modern cloud platforms
• Expert-level proficiency in Python and SQL
• Strong software engineering background with proficiency in Python for building custom integrations and data tools
• Deep production experience with dbt for data modeling and Airflow for orchestration
• Advanced knowledge of cloud-native data suites within GCP (preferred), AWS, or Azure
• Proficiency in managing environment reproducibility using Terraform and Docker, ensuring pipelines are CI/CD compliant
• Expert knowledge of cloud data warehouses (BigQuery or Snowflake) and their internal architecture
• Experience with AI workflows and data systems like vector databases, PostgreSQL, Vertex AI, Pub/Sub

Qualifications

• 6+ years of data engineering experience with a focus on modern cloud platforms.

Nice to Have

• Experience architecting feature stores or data pipelines specifically for ML workloads
• Ability to navigate diverse technical environments and corporate cultures while maintaining a high standard of delivery and client satisfaction
• Commitment to software engineering best practices, including version control, unit testing, and comprehensive documentation
• Analytical thinker who anticipates scaling bottlenecks and security vulnerabilities

Skills

* Required skills

Benefits