Siemens Hiring: AI Data Analytics Engineer 2026:-
An opportunity is available for the position of AI Data Analytics Engineer in Bengaluru, Karnataka, India. This role focuses on data engineering, analytics engineering, AI enablement, enterprise data governance, and scalable data product development using Azure and Databricks technologies.
Complete job details are provided below.
| Job Title: | AI Data Analytics Engineer |
| Company Name: | Siemens Healthineers |
| Job Category: | Research & Development |
| Location: | Bengaluru, Karnataka, India |
| Work Type: | Permanent |
| Educational Qualification: | BE / B.Tech / MCA / ME / M.Tech |
| Experience Required: | 8+ years in Data Engineering / Analytics Engineering |
About the Role:-
The AI Data Analytics Engineer role focuses on building scalable data solutions that power business analytics, dashboards, recommendations, and AI-driven applications.
The selected candidate will design, develop, and optimise data products within a modern Azure and Databricks Lakehouse architecture. The role involves managing the complete data lifecycle, from data ingestion and transformation to quality validation, governance, and delivery of curated datasets for business intelligence and AI applications.
The engineer will work closely with business stakeholders, analytics teams, product teams, and engineering professionals to establish trusted sources of data, standardise business metrics, and deliver reusable, maintainable data solutions.
The position also involves preparing datasets for AI agents and Generative AI applications, supporting Azure AI Foundry integration patterns, automating analytical workflows, and ensuring that data-driven insights support measurable business outcomes.
Educational Requirements:-
Candidates must have one of the following qualifications:
- Bachelor of Engineering (BE)
- Bachelor of Technology (B.Tech)
- Master of Computer Applications (MCA)
- Master of Engineering (ME)
- Master of Technology (M.Tech)
Experience Requirement: A minimum of 8+ years of experience in Data Engineering or Analytics Engineering is specified in the job description.
Key Skills:-
1. Data Engineering and Platform Skills
- Apache Spark 3.x
- Spark DataFrames and Spark SQL
- Batch Processing
- Structured Streaming
- Databricks Workflows
- Databricks SQL Warehouses
- Delta Live Tables (DLT)
- Unity Catalog
- Auto Loader
- Databricks Pipelines
- Azure Data Services
- Azure Data Lake Storage (ADLS)
- Lakehouse Architecture
- Medallion Architecture
- Parquet and Delta Lake
- Data Partitioning and Compaction
- Data Pipeline Development
- Performance Optimisation
- Scalable Data Processing
2. Programming and Analytics Skills
- Strong Python Programming
- SQL Programming
- Spark SQL
- T-SQL
- HiveQL
- Exploratory Data Analysis (EDA)
- Data Quality Management
- KPI-Driven Analytical Modelling
- Statistical Concepts
- Data Profiling and Validation
- Analytical Data Modelling
- Reusable Data Components
- Analytics-Ready Dataset Development
- AI-Ready Dataset Preparation
- Data Transformation and Processing
3. Enterprise Data Governance and Metric Management
- Enterprise Data Governance
- Data Quality Standards
- Data Ownership and Accountability
- Standardised Business Metric Definitions
- KPI Calculation Methodologies
- Trusted Sources of Truth
- Cross-System Data Reconciliation
- Data Consistency and Accuracy
- Data Quality Root Cause Analysis
- Data Remediation
- Metadata Management
- Data Cataloguing
- Governed Data Products
4. AI and Generative AI Skills
- Azure AI Foundry Integration
- AI Agent Data Preparation
- Generative AI Data Enablement
- AI-Driven Analytical Workflows
- Automated Reporting
- Insight Generation
- Business Risk Identification
- Performance Gap Analysis
- Recommendation Use Cases
- Feature Engineering Concepts
- Semantic and Metadata-Driven Datasets
5. Business Intelligence and Reporting Skills
- Qlik
- Microsoft Power BI
- Tableau
- Dashboard Performance Optimisation
- Business Intelligence Data Modelling
- Semantic Data Models
- KPI Reporting
- Business Performance Analysis
- Actionable Insights and Recommendations
- Governed Metric Consumption
6. Testing and DevOps Skills
- pytest
- Great Expectations
- Acceptance Testing
- Data Quality Testing
- Automated Testing Frameworks
- CI/CD Pipelines
- Azure DevOps
- YAML Pipelines
- Version Control
- Engineering Best Practices
- Agile and Scrum Methodologies
- Release Management
7. Additional Preferred Skills
- Azure Data Lake Storage (ADLS)
- Azure Managed Identity
- Azure AI Foundry
- Apache Airflow
- Azure Data Factory (ADF)
- Azure Synapse Pipelines
- Scala or Java
- Microsoft Purview
- Unity Catalog
- Apache Atlas
- Healthcare Domain Experience
Roles and Responsibilities:-
1. Data Engineering and Pipeline Development
- Design, develop, and optimise scalable data pipelines using Databricks and Azure data services.
- Build robust data ingestion, transformation, and processing workflows for enterprise data platforms.
- Integrate internal and external data sources to support business analytics and AI-driven use cases.
- Develop reusable, modular data components that improve maintainability and scalability.
- Implement data processing solutions using Apache Spark, Spark SQL, and Python.
- Develop batch and streaming data processing solutions based on business requirements.
- Optimise Spark workloads, Delta tables, partitioning, compaction, and low-latency data processing.
- Support modern Lakehouse and Medallion Architecture implementations.
2. Data Quality Management and Root Cause Analysis
- Lead data quality initiatives to identify inconsistencies across data sources and pipelines.
- Perform root cause analysis to investigate data defects and reliability issues.
- Develop and implement remediation strategies to resolve data inconsistencies.
- Improve data accuracy, completeness, consistency, and trustworthiness.
- Conduct exploratory data analysis (EDA) to identify patterns, anomalies, and potential data quality issues.
- Establish validation and testing practices to improve the reliability of analytics outputs.
- Monitor data quality and implement improvements to prevent recurring issues.
- Ensure datasets are suitable for downstream analytics, reporting, recommendations, and AI applications.
3. Enterprise Data Governance and Metric Management
- Establish and enforce enterprise data governance standards across systems and data products.
- Collaborate with business and analytics stakeholders to define data ownership.
- Standardise definitions and calculation methodologies for key performance indicators (KPIs) and core business metrics.
- Establish consistent sources of truth across different systems.
- Ensure datasets, business metrics, and analytical outputs remain accurate and consistent.
- Define and maintain governed data products for enterprise-wide consumption.
- Support metadata management, data cataloguing, and consistent metric definitions.
- Promote reliable, reusable, and well-documented data assets.
4. Analytics Engineering and Data Product Development
- Develop curated, consumption-ready datasets for analytics, dashboards, and business reporting.
- Design and maintain reusable data products for business intelligence and AI-driven applications.
- Structure datasets to support analytical modelling, recommendations, and decision-making.
- Develop semantic and metadata-driven datasets for downstream BI and AI consumption.
- Ensure that data products meet business requirements and agreed quality standards.
- Work with analytics and product teams to improve data usability and accessibility.
- Enable consistent consumption of trusted data across business functions.
5. AI and Generative AI Enablement
- Prepare and structure datasets for AI agents and Generative AI applications.
- Support Azure AI Foundry integration patterns and downstream AI consumption.
- Implement AI-driven workflows to automate data analysis and reporting.
- Enable automated insight generation using structured and curated datasets.
- Support the identification of business risks, inefficiencies, and performance gaps through analytical workflows.
- Prepare data assets for recommendation systems and AI-powered use cases.
- Apply metadata and semantic structures that help downstream AI applications consume governed data.
- Translate analytical findings into actionable, data-driven recommendations.
6. Business Intelligence and Dashboard Optimisation
- Support Qlik, Power BI, and Tableau workloads by providing reliable and well-structured datasets.
- Optimise data models and processing workflows that support dashboard performance.
- Ensure dashboards and reports use consistent, governed business metrics.
- Work with business stakeholders to align reporting outputs with organisational priorities.
- Validate that reports, recommendations, and analytical insights are accurate and actionable.
- Identify opportunities to improve reporting efficiency and analytical performance.
- Support measurable business outcomes through trusted data and relevant insights.
7. Testing, CI/CD, and Engineering Best Practices
- Implement testing frameworks to validate data pipelines and analytical outputs.
- Use tools such as pytest and Great Expectations where applicable.
- Support acceptance testing and automated data quality validation.
- Implement CI/CD workflows using Azure DevOps and YAML pipelines.
- Apply version control and structured development practices.
- Improve deployment reliability and maintainability of data engineering solutions.
- Follow engineering standards for testing, documentation, monitoring, and release management.
- Collaborate with teams to resolve issues and continuously improve delivery quality.
8. Business Partnership and Stakeholder Collaboration
- Act as a bridge between data engineering teams and analytics or product consumers.
- Partner with business stakeholders to understand data requirements and reporting priorities.
- Collaborate with analytics teams to define KPIs and standardised business metrics.
- Translate business needs into scalable data engineering and analytics solutions.
- Communicate technical findings and analytical insights clearly to stakeholders.
- Recommend data-driven actions and risk mitigation strategies.
- Ensure that delivered solutions align with business priorities and measurable outcomes.
9. Agile Delivery and Continuous Improvement
- Participate in Agile and Scrum activities, including planning, estimation, and release management.
- Coordinate with cross-functional engineering, analytics, and product teams.
- Identify opportunities to improve pipeline performance and data processing efficiency.
- Contribute to continuous improvement of development and operational processes.
- Support timely delivery of scalable, reliable, and maintainable data solutions.
- Document technical approaches, data definitions, and engineering decisions.
- Take ownership of assigned data products throughout their lifecycle.
Eligibility Criteria:-
Candidates should meet the following requirements:
- Hold a BE, B.Tech, MCA, ME, or M.Tech qualification.
- Have at least 8+ years of experience in Data Engineering or Analytics Engineering.
- Possess strong Python and SQL programming skills.
- Have hands-on experience with Apache Spark 3.x.
- Have experience working with Databricks and Azure data services.
- Understand Lakehouse and Medallion Architecture concepts.
- Have experience developing and optimising scalable data pipelines.
- Understand data quality, exploratory data analysis, and KPI-driven analytical modelling.
- Have experience building analytics-ready and AI-ready datasets.
- Understand enterprise data governance and standardised business metric management.
- Be capable of collaborating with business, analytics, product, and engineering teams.
- Have experience with BI workloads, testing practices, and CI/CD processes, as relevant to the role.
Siemens Hiring: AI Data Analytics Engineer 2026 Application Process:-
Interested candidates can apply from the given below link.
Apply Link:- Click Here To Apply
Note:- Only the shortlisted candidates will be notified for the further interview process.
