Data Architecture Migration: Lead the architectural shift from Salesforce Datorama to an enterprise modern data stack (Fivetran + AWS S3 + Databricks + Tableau).

Ingestion Integration: Configure and manage automated Fivetran connectors to pull data from diverse paid media channels (e.g., Google Ads, Meta Ads, LinkedIn Ads, programmatic media) and CRM/CRMA sources into AWS S3 raw storage.

Databricks Lakehouse Data Pipelines: Design, build, and optimize PySpark/SQL data pipelines within Databricks to transform raw landing data into clean, aggregated, and business-ready reporting models.

Tableau Reporting Integration: Map and repoint existing Salesforce Datorama reporting logic to the new Databricks gold-layer tables and view structures in Tableau Desktop/Server.

Data Quality Validation:



Perform rigorous end-to-end data validation and reconciliation between historical Datorama dashboards and recent Tableau dashboards to ensure data parity and metric consistency.

Performance Optimization: Optimize Databricks clusters, Delta Lake tables, and SQL queries to minimize processing runtime and cloud resource costs.

Collaboration Documentation: Work closely with marketing analysts, business stakeholders, and client leadership to map legacy Datorama metrics to the new target schemas and document pipeline workflows.

Required skills

Databricks, PySpark, and SQL,Fivetran, AWS S3, Tableau

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Analyst - Data Engineering (Chennai)
🏢 Latentview
📍 Chennai