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Job details
Qualification
Master's (Engineering) / MCA
Job ID
P-100743
Job description
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
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
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)
Required skills
spark 3.x, databricks, azure data services, python, sql, data engineering, data pipeline development, data quality management, data governance, azure ai foundry, parquet, delta lake, data catalogs, azure devops, etl, data warehousing, data modeling, statistical analysis, kpi development, data analytics, qlik, power bi, tableau, great expectations, acceptance testing, yaml pipelines, agile, scrum, feature engineering
