Data and Analytics Engineer, Enablement

Zensar Technologies Limited is seeking a highly skilled Data and Analytics Engineer to enable data engineers and analysts through best‑practice data engineering in Microsoft Fabric and Databricks, and to create high‑quality Power BI visualisations. The role collaborates with stakeholders to translate business needs into robust data products, semantic models, and self‑service analytics, while driving modernisation of legacy BI assets and exploring AI‑driven insights.
8+ years of experience in data engineering and analytics, preferably in enterprise environments.
Hands‑on expertise with Microsoft Fabric, Databricks, Delta Lake, and PySpark.
Strong SQL skills and experience implementing CDC and incremental load patterns.
Proficiency in Power BI development, DAX, and designing star‑schema data models.
Experience with CI/CD tools (e.g., Azure DevOps, Git) and Python automation scripting.
Knowledge of data governance, security best practices, and data quality frameworks.
Ability to translate business requirements into technical specifications and KPIs.
Excellent communication and stakeholder management skills.
Familiarity with AI/ML integration for conversational analytics is a plus.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
Build and maintain bronze, silver, and gold lakehouse layers in Microsoft Fabric and Databricks using notebooks and PySpark.
Implement incremental loading and change‑data‑capture pipelines from on‑prem SQL sources to Fabric/Databricks, optimizing Delta tables for performance and cost.
Translate business requirements into analytical specifications, define KPIs, and validate data product outputs during SQL‑to‑Lakehouse modernization.
Design and govern Fabric semantic models and star schemas to support performant Power BI self‑service reporting.
Develop, optimize, and maintain interactive Power BI dashboards, including complex DAX calculations, row‑level security, and usage monitoring.
Create reusable Python scripts, CI/CD pipelines, and automation for data processing, report generation, and integration with external APIs.
Define and deliver self‑service analytics playbooks, training materials, and governed templates to empower business users.
Establish playbooks for conversational and AI‑driven analytics, ensuring semantic models support natural language queries and AI‑generated insights.
Support migration of legacy SSRS, SSAS, and SQL‑based reports to modern Power BI and lakehouse solutions, validating parity and decommissioning artifacts.
Continuously improve data processes, promote standards, and contribute to COE knowledge‑sharing initiatives focused on data quality, governance, and security.