Experience
7 - 10 yrs
Job Location
Bengaluru, India
Vacancy
1
Designation
Senior Artificial Intelligence Engineer
Job Type
Not specified
Job Description
Senior AI Data Engineer
This role has been designed as Onsite with an expectation that you will primarily work from an HPE office.
Role Summary
The Senior Data Scientist role is an individual contributor role that acts as technical subject matter across the full data platform stack - data architecture, transformation design, data quality frameworks, and governance fit for both analytical and AI consumption. The role will work hand in glove with the AI engineers and will play a pivot role in enabling high quality and accuracy data for AI products. This role will also act as a technical mentor for the data engineering team and a trusted partner to the Senior AI & ML Engineer jointly ensuring that governed, high-quality data reliably powers both reporting and AI use cases.
What youll do:
Technical Leadership & Data Architecture
- Serve as the data engineering SME the escalation point for complex platforms, pipeline, and governance decisions across the team and organization.
- Architect end-to-end data solutions: Design reusable data components, design Lakehouse structures, data vault patterns, semantic layers, and integration of architectures across AI, Analytics and Automation platforms.
- Define and enforce data engineering standards, pipeline design patterns, naming conventions, and coding best practices; conduct architecture and code reviews.
- Lead technical discovery for new data initiatives: assess feasibility, design solution approaches, and produce architecture documentation for stakeholder alignment.
- Mentor and upskill the Technical Data Engineer through structured knowledge transfer, pair programming, and design reviews.
Advanced Data Transformation & Pipeline Engineering
- Design and deliver complex, production-grade ELT/ETL pipelines using Databricks (Delta Live Tables, PySpark, Unity Catalog) and Microsoft Fabric (Dataflows Gen2, Notebooks, Data Factory).
- Architect reusable, parameterized pipeline frameworks that the wider team can adopt reducing one-off scripting and increasing delivery velocity.
- Define and implement advanced transformation patterns: multi-hop Delta Lake pipelines, SCD Type 2/6, event-driven streaming ingestion, and late-arriving data handling.
- Optimize pipeline performance at scale partitioning strategy, Z-ordering, liquid clustering, broadcast joins, and cost-based query planning in Spark.
Data Quality Strategy & Governance Leadership
- Define the AI products data quality framework establish DQ dimensions, thresholds, escalation paths, and remediation SLAs across all critical datasets.
- Drive business glossary completeness, lineage documentation, data stewardship workflow design, and policy management.
- Implement automated data quality validation at scale. Integrate DQ gates into CI/CD pipeline deployments.
- Act as the data quality escalation authority triage complex DQ incidents, perform root-cause analysis, and drive permanent fixes rather than tactical workarounds.
Power BI & Advanced Analytics Reporting
- Lead the Power BI governance model: certified dataset strategy, endorsement policies, deployment pipelines, and report lifecycle management.
- Design and review complex semantic models for performance, correctness, and scalability; define DAX patterns and measure libraries for team-wide reuse.
- Translate complex analytical requirements from senior stakeholders into governed, self-service-ready data products.
- Drive adoption of Copilot-assisted authoring in Power BI and Fabric; evaluate AI-assisted analytics features and recommend adoption roadmaps.
- AI Data Enablement & Cross-functional Partnership
- Partner with the Senior AI & ML Engineer to architect data foundations for LLM and ML use cases defining feature stores, embedding
No Referrers Available
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