[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Tata Consultancy Services jobs](https://jobs.opennash.com/companies/tata-consultancy-services/) · [Search jobs](/search/) Tata Consultancy Services · Auckland # Senior Data Engineer Auckland Senior individual contributor Data & Analytics Last seen on Tata Consultancy Services’s official careers site on August 1, 2026; it may have closed since. [Apply on Tata Consultancy Services’s careers site →](https://ibegin.tcsapps.com/candidate/jobs/415414J) [OpenNash Jobs](https://jobs.opennash.com/) / [Tata Consultancy Services](https://jobs.opennash.com/companies/tata-consultancy-services/) / Senior Data Engineer ## Job description ### Key Responsibilities - · Pipeline Development: Build and maintain robust ETL/ELT pipelines using Databricks (PySpark/Spark SQL), and related Azure data services. Ensure pipelines handle batch and streaming workloads reliably at scale. ### Legacy Modernisation - Lead the migration of legacy data pipelines to modern cloud-native solutions on Azure and Databricks. Define migration strategies, manage cutover plans, and ensure zero data loss during transition. ### Data Quality & Governance - Establish and enforce data quality frameworks, validation rules, and monitoring across all pipelines. Implement data lineage tracking, cataloguing (Unity Catalog), and access governance in line with organisational policies. ### Data Modelling & Design - Design logical and physical data models for analytical and operational workloads. Define schemas, partitioning strategies, and optimisation patterns for performance and cost efficiency across Delta Lake and Azure SQL. ### Stakeholder Engagement -: Engage with the client’s Data & Analytics team on technical discussions, solution walkthroughs, and progress updates. Participate in requirement workshops and provide technical input to support estimation and planning. ### DevOps & Automation - Build and maintain CI/CD pipelines for data workloads using Azure DevOps. Implement automated testing for data pipelines, and deployment automation across environments. ### Mentorship & Standards - Review code, enforce engineering standards, and mentor offshore and onsite data engineers. Drive adoption of best practices for Spark, SQL, Python, testing, and version control across the team.