Roles & Responsibilities Lead the analysis and migration of legacy SQL, ETL, Data Warehouse, and T-SQL based workloads to Databricks. Assess complex T-SQL scripts, stored procedures, functions, views, dependencies, and transformation logic for modernization. Design scalable Databricks and PySpark solutions aligned to migration strategy and enterprise data architecture standards. Translate legacy business logic into optimized PySpark, Spark SQL, Delta Lake, and Databricks workflows. Create reusable frameworks, patterns, and best practices for migration from legacy platforms to Databricks. Provide technical guidance to mid-level Data Engineers and review migration deliverables for quality and performance. Collaborate with architects, stakeholders, business users, and delivery teams to define migration roadmaps and source-to-target mappings. Drive performance tuning, workload optimization, debugging, and scalability improvements across Databricks workloads. Ensure data quality, reconciliation, governance, security, and production readiness throughout the migration lifecycle. Support estimation, planning, risk identification, documentation, and technical decision-making for the program. Mentor team members and help establish strong Data Engineering practices across the project. Must Have Skills 7+ years of relevant Data Engineering experience, with a consistent Data Engineering career path. Expert-level hands-on experience with Databricks. Strong PySpark expertise for enterprise-scale data processing and transformation. Advanced SQL/T-SQL understanding, with robust ability to read, analyze, and interpret legacy T-SQL implementations. Experience in migration or modernization from legacy SQL/ETL/Data Warehouse platforms to Databricks or modern cloud data platforms. Strong background in SQL Server, Oracle, ETL tools, Data Warehousing,



or similar legacy data ecosystems. Strong understanding of Data Modeling, Data Warehousing, Delta Lake concepts, and batch processing patterns. Experience designing and optimizing scalable ETL/ELT pipelines. Strong performance tuning and troubleshooting skills in Spark/Databricks environments. Ability to guide engineering teams, review code, and influence technical solutions. Strong communication and stakeholder management skills. Preferred Skills Azure Data Factory and broader Azure Data Platform expertise. Unity Catalog, data governance, lineage, and security exposure. Azure Synapse Analytics experience. CI/CD and DevOps practices for Data Engineering workloads. Experience creating migration frameworks, accelerators, or reusable transformation patterns. Databricks certification or Azure Data Engineer certification. Agile delivery and technical leadership experience. Qualifications Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline. Relevant certifications in Databricks, Azure Data Engineering, Cloud Data Engineering, or Data Architecture will be preferred. Ideal Candidate Profile / Screening Notes Strong, hands-on Data Engineering background throughout career, not a partial or recent transition. Started career in legacy database, SQL Server, Oracle, ETL, or Data Warehouse environments and later moved into modern Data Engineering. Can read, understand, and interpret T-SQL logic, stored procedures, views, and transformation flows for migration purposes. Does not need to be primarily focused on writing fresh T-SQL code, but must be able to analyze existing T-SQL and convert the logic to Databricks/PySpark. Strong Databricks and PySpark experience with practical migration or modernization exposure. Avoid profiles that are mainly BI Developers, Power BI Developers, Data Analysts, or reporting-only candidates.

📌 Data Engineers with Nuvento (Kochi)
🏢 Nuvento Systems
📍 Kochi