Key Responsibilities
- Build & Scale: Develop and maintain end-to-end data pipelines spanning source systems, ingestion, transformation, storage, and serving layers for enterprise-scale analytics and reporting.
- Lakehouse Implementation: Implement and optimize up-to-date data platforms using medallion architecture (Bronze/Silver/Gold) on Databricks, Microsoft Fabric, or Snowflake, leveraging Delta Lake / ACID-compliant storage.
- Pipeline Engineering: Build and manage robust ETL/ELT pipelines using Azure Data Factory, Fabric Data Factory, or equivalent orchestration tools, including data extraction from SAP, SAP BusinessObjects, and other enterprise source systems.
- Data Modeling & Quality: Apply advanced data modeling standards (dimensional, normalized, and lakehouse models) and implement automated frameworks for data quality, lineage, and observability.
- BI & Serving Integration: Build and optimize semantic and reporting layers for BI consumption,



including Power BI Direct Lake, SQL Analytics Endpoints, and data warehouse / lakehouse serving layers.
- Security & Governance: Implement data governance, security, access controls, and compliance frameworks (such as GDPR/DPDP) within the data pipelines and storage layers.
- Team Leadership & Mentorship: Lead and mentor junior/mid-level data engineers, championing best practices in PySpark/SQL coding standards, performance tuning, and cloud cost optimization.
- DevOps & CI/CD: Drive DevOps best practices for data (DataOps), including automated testing, CI/CD pipeline development, and Infrastructure as Code (IaC).
- PoC to Production: Lead proof-of-concept (PoC) initiatives and rapidly transition them into reliable, fault-tolerant, and high-performance production deployments.

📌 Lead Data Engineer (Pune)
🏢 Wishtree Technologies
📍 Pune