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, Excellent 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)

📌 AI Data Analytics Engineer (Bengaluru)
🏢 Siemens Healthineers
📍 Bengaluru