About this role
Job Requirements We are looking for a Senior AI Data Analytics Engineer with strong Data Engineering expertise, analytical thinking, and AI enablement capabilities to build scalable data solutions that power analytics, dashboards, recommendations, and AI-driven use cases. The role involves designing and evolving data products within a modern Azure + Databricks Lakehouse architecture, enabling business insights and AI solutions through curated, consumption-ready datasets. The ideal candidate will own the end-to-end data lifecycle and work closely with business, product, and engineering teams to deliver scalable and maintainable solutions. Qualification • BE / B.Tech / MCA / ME / M.Tech • 8+ years of experience in Data Engineering / Analytics Engineering Key Responsibilities • Lead data quality and governance initiatives, including root-cause analysis, remediation of data inconsistencies, reliability improvements, and exploratory data analysis (EDA) • Collaborate with business and analytics stakeholders to define ownership, standardized definitions, and calculation methodologies for KPIs and core business metrics • Establish consistent sources of truth across systems and ensure the accuracy, consistency, and trustworthiness of datasets, metrics, and analytics outputs • Design, develop, and optimize scalable data pipelines using Databricks and Azure data services • Integrate internal and external data sources and build reusable, modular data components • Develop curated datasets and data products for analytics, dashboards, recommendations, and AI applications • Design batch and streaming solutions; optimize Spark workloads, Delta tables, and low-latency data processing • Prepare and structure datasets for AI Agents / GenAI and support Azure AI Foundry integration patterns • Implement AI-driven workflows to automate data analysis, reporting, insight generation, and identification of business risks, inefficiencies, and performance gaps • Design semantic and metadata-driven datasets and enable downstream AI and BI consumption • Ensure dashboards, reports, insights, and recommendations are actionable, aligned with business priorities, and support measurable outcomes • Support Qlik / BI performance optimization and consistent consumption of governed business metrics • Implement testing, CI/CD, version control, and engineering best practices using Azure DevOps • Participate in agile delivery including planning, estimation, releases, and cross-functional collaboration Required Skills Data Engineering & Platform • Spark 3.x (DataFrames, SQL, Batch & Structured Streaming) • Databricks (Workflows, SQL Warehouses, DLT, Unity Catalog, Auto Loader, Pipelines) • Azure Data Services and Lakehouse / Medallion Architecture • Parquet / Delta, partitioning, compaction, and performance optimization Programming & Analytics • Strong Python and SQL (Spark SQL, TSQL, HiveQL) • Data quality, EDA, KPI-driven analytical modeling • Understanding of statistical concepts and data readiness for analytics/recommendation use cases • Experience building reusable, analytics-ready, and AI-ready datasets Enterprise Data Governance & Metric Management • Establishing and enforcing enterprise data governance, data quality standards, and consistent sources of truth across systems • Defining ownership, standardized definitions, and calculation methodologies for core business metrics • Leading data quality initiatives to identify and remediate inconsistencies across data sources and pipelines, ensuring accurate, consistent, and trusted analytics outputs Business Partnership & Outcome Accountability • Act as the bridge between the engineering team and the analytics/product consumers. • Partnering with business and analytics stakeholders to align data, reporting, dashboards, and AI solutions with business priorities and measurable outcomes • Using AI-driven workflows to automate data analysis, reporting, insight generation, and identification of risks, inefficiencies, and performance gaps • Translating analytical findings into actionable, data-driven recommendations and risk mitigation strategies AI, BI & Delivery • Azure AI Foundry integration and AI/Agent data preparation • Experience supporting Qlik / Power BI / Tableau workloads • Testing frameworks (pytest, Great Expectations, Acceptance Testing) • CI/CD with Azure DevOps and YAML pipelines • Agile/Scrum development practices Good to Know • ADLS, Managed Identity, Azure AI Foundry • Feature engineering concepts • Airflow / ADF / Synapse Pipelines • Scala or Java • Data Catalogs (Purview, Unity Catalog, Apache Atlas) • Healthcare domain experience (preferred)
Work Experience Required Skills Data Engineering & Platform • Spark 3.x (DataFrames, SQL, Batch & Structured Streaming) • Databricks (Workflows, SQL Warehouses, DLT, Unity Catalog, Auto Loader, Pipelines) • Azure Data Services and Lakehouse / Medallion Architecture • Parquet / Delta, partitioning, compaction, and performance optimization Programming & Analytics • Strong Python and SQL (Spark SQL, TSQL, HiveQL) • Data quality, EDA, KPI-driven analytical modeling • Understanding of statistical concepts and data readiness for analytics/recommendation use cases • Experience building reusable, analytics-ready, and AI-ready datasets Enterprise Data Governance & Metric Management • Establishing and enforcing enterprise data governance, data quality standards, and consistent sources of truth across systems • Defining ownership, standardized definitions, and calculation methodologies for core business metrics • Leading data quality initiatives to identify and remediate inconsistencies across data sources and pipelines, ensuring accurate, consistent, and trusted analytics outputs Business Partnership & Outcome Accountability • Act as the bridge between the engineering team and the analytics/product consumers. • Partnering with business and analytics stakeholders to align data, reporting, dashboards, and AI solutions with business priorities and measurable outcomes • Using AI-driven workflows to automate data analysis, reporting, insight generation, and identification of risks, inefficiencies, and performance gaps • Translating analytical findings into actionable, data-driven recommendations and risk mitigation strategies AI, BI & Delivery • Azure AI Foundry integration and AI/Agent data preparation • Experience supporting Qlik / Power BI / Tableau workloads • Testing frameworks (pytest, Great Expectations, Acceptance Testing) • CI/CD with Azure DevOps and YAML pipelines • Agile/Scrum development practices Good to Know • ADLS, Managed Identity, Azure AI Foundry • Feature engineering concepts • Airflow / ADF / Synapse Pipelines • Scala or Java • Data Catalogs (Purview, Unity Catalog, Apache Atlas) • Healthcare domain experience (preferred)