India · Senior
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| Job Purpose | To effectively design, develop, and manage data solutions using ETL technologies such as Azure Databricks (ADB) , Azure Data Factory (ADF) and SQL along with leading 3 to 5 members of developers |
| Duties and Responsibilities | KEY RESPONSIBILITIES • Lead the end-to-end design, development, and delivery of scalable data engineering solutions using Azure Databricks, ADF, PySpark, SQL, and Delta Lake. • Convert business requirements into robust technical designs, architecture documents, data models, and implementation plans. • Own technical delivery of data integration, ETL, lakehouse, semantic layer, and AI/BI enablement initiatives. • Guide and mentor data engineers on coding standards, design best practices, performance optimization, and reusable framework development. • Review technical designs, code, pipelines, and deployment plans to ensure quality, scalability, maintainability, and compliance. • Drive architecture decisions for batch and near-real-time data pipelines across Bronze, Silver, and Gold layers. • Ensure data quality, reconciliation, anomaly detection, and timely resolution of production issues through effective RCA and permanent fixes. • Optimize data pipelines, Databricks jobs, SQL queries, and storage usage to improve performance and reduce cost. • Implement CI/CD practices, version control, automated deployments, and environment management across Dev, QA, and Production. • Collaborate with PMO, business stakeholders, BI teams, InfoSec, DevOps, and external partners for smooth project execution. • Establish SOPs, engineering standards, reusable components, monitoring frameworks, and documentation practices. • Track delivery progress, manage technical dependencies, prioritize work, and ensure timely closure of project milestones. • Support adoption of modern data platforms, semantic modeling, metrics layer design, and GenAI/BI capabilities. • Ensure compliance with data governance, security, access control, audit, and enterprise data management standards. • Act as the technical escalation point for critical issues, complex solutioning, and cross-team dependency resolution. |
| Key Decisions / Dimensions | KEY DECISIONS / DIMENSIONS • Define semantic layer design and metric definitions • Prioritize data vs AI optimization trade-offs • Handle production issues with RCA and long-term fixes • Drive architectural decisions for lakehouse + Data integration |
| Major Challenges | MAJOR CHALLENGES • Ensuring Data Delivery within TAT • Driving adoption of GenAI-based BI over traditional dashboards • Balancing performance, cost, and scalability • Managing dependencies across data engineering, AI, and business teams |
| Required Qualifications and Experience | REQUIRED SKILLS & EXPERIENCE Must Have • Azure Databricks – PySpark, SQL, Delta Lake • Strong experience in Semantic Modeling & Metrics Layer design • Hands-on with Databricks workflows • Pyspark (Pandas, PySpark, FastAPI) • Azure Data Factory (ADF) for ETL pipelines • Strong SQL and data modeling skills Good to Have • Cosmos DB / MongoDB (NoSQL concepts) • Azure Data Explorer (KQL) DATA STACK (MANDATORY FOR SCREENING) SNo Data Platform / Concepts Associated Technologies 1 Databricks Lakehouse PySpark, SQL, Delta Lake 2 AI for BI Databricks Genie, Genie Rooms, Instructions, Agents 4 ETL & Orchestration Azure Data Factory 5 Programming Pyspark 6 Cloud Platform Azure (Preferred) 10 DevOps CI/CD Pipelines, Git |
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