The Senior Data Engineer is responsible for designing, building, and operating scalable and reliable data pipelines and data products that support enterprise analytics, reporting, and AI/ML use cases. This role is a senior technical contributor responsible for implementing complex data pipelines, improving platform reliability, and ensuring data quality across the enterprise data platform. The Senior Data Engineer partners closely with data architects, analytics engineers, and platform teams to deliver trusted, production-ready data assets. This role requires strong technical expertise, ownership of complex engineering workstreams, and the ability to mentor junior engineers while maintaining high standards of engineering quality and operational reliability.
• Design and build scalable batch and streaming data pipelines from enterprise source systems to the data platform
• Implement ingestion, transformation, and enrichment processes using approved architectural patterns
• Optimize pipelines for performance, reliability, and cost efficiency
• Develop reusable frameworks and components to accelerate pipeline development
• Implement data engineering solutions aligned with lakehouse / medallion architecture patterns (Bronze, Silver, Gold layers)
• Support integration of enterprise systems with the modern data platform
• Partner with platform engineering teams to improve pipeline performance and platform scalability
• Contribute to engineering patterns and reusable templates across the data platform
• Implement data validation, reconciliation, and monitoring processes
• Ensure data pipelines meet enterprise data quality, reliability, and observability standards
• Troubleshoot pipeline failures and support root cause analysis
• Help define and maintain SLAs for critical data pipelines and data products
• Write clean, well-tested, and maintainable code
• Implement engineering best practices including version control, CI/CD pipelines, automated testing, and infrastructure-as-code
• Conduct peer code reviews and maintain engineering standards
• Partner closely with Data Architects, Analytics Engineers, Data Scientists, Platform Engineering teams, and product & business stakeholders
• Translate business requirements into scalable technical solutions
• Provide technical guidance during sprint planning and backlog refinement
• Ensure delivery timelines are met and proactively manage risks or blockers
• Define and maintain standards for data pipeline documentation, data lineage and metadata, and operational procedures
• Ensure compliance with data governance, security, and enterprise architecture standards
• Contribute to engineering playbooks, standards, and knowledge sharing
Minimum Education and/or Experience:
• 5–8+ years of experience in data engineering, analytics engineering, or software engineering
• Proven experience building and maintaining production-grade data pipelines in modern data platforms
• Experience working in Agile delivery environments
• Experience supporting enterprise analytics or AI/ML data use cases
Additional knowledge and skills:
• Strong SQL expertise and experience designing complex transformations
• Proficient programming skills in Python, Scala, or similar languages
• Experience with modern data platforms such as Databricks, Snowflake, or BigQuery
• Experience with ELT / ETL frameworks patterns, workflow orchestration tools, data quality and observability frameworks
• Experience with cloud platforms (AWS, Azure, or GCP)
• Strong understanding of lakehouse / medallion architectures
• Experience implementing large-scale data lakehouse platforms
• Experience supporting AI/ML workloads and feature pipelines
• Experience with data governance and metadata management tools
• Cloud or data platform certifications
