AI Data Engineer

AI Data Engineer

  

This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.

Job Description:

   

HPE Financial services is where we help organizations create the investment they need for digital transformation, in an innovative and sustainable way. We partner with customers across their entire IT asset portfolio from edge to cloud to end-user. Unique to each client’s aspirations and size, our financial and asset management solutions are anchored by best-in-class tech upcycling services. Join us redefine what’s next for you. 

Role summary

We are looking for a technically sharp and detail-oriented Data Engineer to join HPEFS (Hewlett Packard Enterprises Financial Services - Advanced Analytics & BI team Bangalore. This role is the data backbone that powers our AI capabilities — working in close partnership with the AI Engineers to ensure that the data flowing into AI models, dashboards, and business workflows is clean, governed, and well-structured. This role will play a hands on role and own the backend data lifecycle: ingesting raw data from diverse sources, transforming it into reliable, analysis-ready datasets, enforcing data quality standards, and publishing governed data products via Microsoft Fabric and Databricks. You will also support reporting needs through Power BI and contribute to Collibra-based data governance initiatives. A working familiarity with Microsoft Copilot and AI-assisted data tooling is expected

What you'll do:

Data Engineering & Transformation

• Design, build, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Databricks (PySpark / Delta Live Tables), and Microsoft Fabric Data Factory.

• Transform raw, multi-source data into clean, conformed, and analytics-ready datasets following Medallion Architecture principles (Bronze → Silver → Gold).

• Develop and optimize SQL and PySpark-based transformation logic for structured, semi-structured, and unstructured data.

• Implement incremental load patterns, merge/upsert logic, and slowly changing dimension (SCD) strategies to support historical data tracking.

• Collaborate with the AI Engineers to prepare high-quality feature datasets for ML and LLM use cases.

Data Quality & Governance

• Define, implement, and monitor data quality rules including completeness, accuracy, consistency, timeliness, and uniqueness checks.

• Administer and extend the Collibra data governance platform — including business glossary management, data lineage documentation, and stewardship workflows.

• Build automated data quality validation frameworks using tools such as Great Expectations, dbt tests, or Unity Catalog data quality constraints in Databricks.

• Triage and resolve data quality incidents, root-cause data anomalies, and communicate impact to stakeholders proactively.

• Maintain metadata catalogues and ensure all critical datasets have documented ownership, lineage, and classification.

Microsoft Fabric & Lakehouse

• Build and manage Lakehouses, Warehouses, and Dataflows Gen2 within the Microsoft Fabric ecosystem.

• Configure OneLake, shortcuts, and mirroring to unify data across sources without unnecessary duplication.

• Leverage Fabric Notebooks (PySpark / Python) and Spark job definitions for large-scale data processing.

• Support the semantic model layer in Fabric to ensure Power BI datasets are optimized and governed

Power BI & Reporting

• Develop and maintain Power BI semantic models (star schema design, DAX measures, row-level security).

• Build production-grade dashboards and reports for business stakeholders; ensure refresh reliability and performance.

• Apply Copilot-assisted authoring in Power BI and Fabric where applicable to accelerate report generation.

• Support self-service analytics adoption by publishing governed datasets to the Power BI service

Collaboration & AI Enablement

• Partner closely with the AI Engineers, peer data scientist and analytics team members to supply clean, structured data for RAG pipelines, model training, and agentic workflows.

• Contribute to the design of shared data contracts and API schemas between data engineering and AI engineering layers.

• Assist with AI-assisted data tasks using Microsoft Copilot (in Fabric, Power BI, and Azure environments).

What you need to bring:

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, Mathematics, or a related discipline.

• 4 – 5 years of hands-on experience in data engineering, ETL development, or analytics engineering roles.

• Demonstrable experience with Databricks and/or Microsoft Fabric in a production environment.

• Proficiency in Power BI report and semantic model development.

• Exposure to Collibra or equivalent data governance / cataloguing platforms is strongly preferred.

• Strong SQL and Python skills; PySpark experience is required.

• Familiarity with Azure cloud services and DevOps practices for data pipeline deployment

Technical Skill Requirements

• Data Platforms - Databricks (PySpark, Delta Lake, Delta Live Tables, Unity Catalog), Microsoft Fabric (Lakehouse, Warehouse, Dataflows Gen2, Notebooks), Azure Data Lake Storage Gen2

• Data Transformation - PySpark, SQL, dbt (data build tool), Azure Data Factory, Fabric Data Factory; Medallion Architecture, SCD types, incremental load patterns

• Data Modelling - Star schema, snowflake schema, dimensional modelling, data vault concepts; normalization, entity-relationship design, semantic layer design

• Reporting & BI - Power BI (DAX, semantic models, RLS, Power Query / M), Microsoft Fabric Power BI integration, Copilot-assisted authoring in Power BI

• Programming - Python (primary), SQL (advanced); PySpark; familiarity with JSON, Parquet, Delta file formats

• Cloud & DevOps - Azure (preferred): Synapse, ADF, ADLS Gen2, Key Vault; Git/GitHub for version control; CI/CD basics for pipeline deployment

• Data Governance & Cataloguing - data lineage documentation, metadata management, data classification and tagging, business glossary ownership

• AI & Copilot Tooling - Microsoft Copilot in Fabric / Power BI; familiarity with AI-assisted data transformation; understanding of LLM data requirements (embeddings, chunking, vector-ready formats)

• Data Concepts - Data warehousing, lakehouse architecture, OLAP vs OLTP, event-driven ingestion, streaming basics (Structured Streaming / Event Hubs), data contracts, master data management (MDM)

#Financialservices

Accountability, Accountability, Action Planning, Active Learning, Active Listening, Agile Methodology, Agile Scrum Development, Analytical Thinking, Bias, Coaching, Creativity, Critical Thinking, Cross-Functional Teamwork, Data Analysis Management, Data Collection Management (Inactive), Data Controls, Design, Design Thinking, Empathy, Follow-Through, Group Problem Solving, Growth Mindset, Intellectual Curiosity (Inactive), Long Term Planning, Managing Ambiguity {+ 5 more}

Health & Wellbeing

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.

Unconditional Inclusion

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

#india

Job:

Engineering

Job Level:

TCP_03

    

    

Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

   

HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.

   

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