Lead Architect - Data and AI Engineering (Location: INDIA, Hybrid)

Position Overview

The Lead Architect owns the end-to-end delivery of the data and AI engineering work on a flagship multi-year enterprise data transformation program. The program is building a unified, governed data foundation on Azure across multiple business domains, with real-time CDC ingestion, master data management, and AI-ready analytics, with retrieval, AI, and agentic workloads built on top of that foundation. This is a builder-leader role: you act as the technical bridge between the customer's senior technology leadership and the Aubrant delivery team, write modeling decisions, get hands-on with Databricks and pipeline code as you lead a data and AI engineering team, and pressure-test the team's QA approach yourself. Beyond client delivery, this role serves as an internal technical leader and coach across Aubrant's Data & AI Studio — raising the bar on AI engineering and agentic patterns, and mentoring engineers.

Delivery Ownership & Execution

• Own end-to-end delivery of the data transformation against agreed architecture, requirements, and schedule

• Translate the architecture and Unified Data Model into an executable plan: source onboarding, ingestion patterns, ELT design, serving patterns, and quality gates

• Drive sprint planning, milestone tracking, and execution across the program's phased delivery

• Identifyrisks, dependencies, and blockers early; drive resolution and manage scope and timeline commitments

Customer & Stakeholder Engagement

• Act as the day-to-day technical point of contact for customer leadership and engineering on progress, blockers, decisions, and solution alternatives

• Run technical working sessions, design reviews, and walkthroughs that move decisions forward

• Translate business context into technical implications, and technical complexity into clear leadership-ready summaries

Architecture, Modeling & Engineering

• Hold a working understanding of the full target tech stack andvalidatethat implementation choices stay consistent with the reference architecture

• Lead and contribute to data modeling across the core enterprise domains; review modeling work for identity, SCD, CDC, PII, and survivorship correctness

• Build production-grade ETL/ELT pipelines on Azure Databricks (PySpark, Spark SQL) with Delta Lake: ingestion, conformance, survivorship, and quality test layers

• Configure and extendAirbyteconnectors for CDC ingestion and integrate API-based sources across SaaS, ERP, HRIS, and operational systems

• ApplyAubrantWorkbench accelerators to compress build time and ensure consistency
• Lead the AI and agentic engineering patterns that sit on top of the data platform: retrieval pipelines, vector indexes, embedding generation, feature stores, and evaluation harnesses for LLM-backed and agentic workloads
• Partner with AI Engineers to operationalize models and agents in production: MLOps lifecycle, prompt and eval versioning, observability, safety and cost guardrails, and clear handoffs between data, model, and application layers

Infrastructure, DevOps & Quality

• Partner with the cloud and DevOps team on what the data team needs from the platform: workspace topology, network and identity, secret management, observability, andcostguardrails

• Ensure CI/CD pipelines for data assets are in place and used: unit and integration tests, lineage validation, environment promotion, automated deployment, and infrastructure-as-code discipline

• Define the QA approach: data quality rules, test data strategy, regression testing, reconciliation against sources, and acceptance criteria for golden records

• Instruct and review QA work; hold the line on quality gates betweenBronze, Silver, Goldtiers and Dev, Test, Prod environments

Leadership & Coordination

• Lead and coordinate a cross-functional pod including:

• Data Architects
• AI / Agentic Engineers

• Data Modeler

• Senior Cloud Engineer

• Data Engineers

• QA Engineers

• Support Agile ceremonies, backlog prioritization, and remove blockers

• Mentor Studio Members and codify reusable patterns into the Studio knowledge base and theAubrantWorkbench across both data engineering and AI / agentic engineering disciplines

Key Qualifications

Experience

• 12+ years in data engineering and data platform delivery, with 5+ years in a Technical Lead or equivalent role on customer-facing engagements

• Multiple end-to-end deliveries of enterprise-scale data platforms, witha track recordof delivering against architecture, schedule, and quality

Required Technical Skills

• Azure Databricks (PySpark, Spark SQL), Delta Lake, the Medallion architecture, and ADLS Gen2: hands-on production experience

• Data modeling: conceptual, logical, and physical, including SCD strategy, CDC patterns, PII classification, and survivorship

• CDC and ingestion: production experience withAirbyte,Fivetran, Azure Data Factory, or equivalent, plus API-based source onboarding

• At least one of Azure Synapse, Cosmos DB, or Azure SQL Managed Instance for serving patterns

• CI/CD for data assets and infrastructure-as-code (Terraform, Bicep, or ARM)