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Location: Pune, India
Job Requirements Data Architect - Databricks, Snowflake & AI for Data On-ground solution leadership | Modern data platforms | AI-enabled data engineering | Customer thought leadership Function Data Architecture & AI-Enabled Data Platforms Level Principal Architect / Data Architect Location Client location / Hybrid, with regular on-ground engagement Reports to Delivery Partner / Data & AI Practice Leader Employment type Full-time Experience guide 16+ years overall, including 5+ years in enterprise data architecture. Databricks with AI for DATA initiatives experience to be on ground as solution lead and thought leader to customer Function Role Purpose The Data Architect will serve as the on-ground solution lead and trusted architecture advisor for enterprise Data & AI initiatives. The role will own target-state architecture across Databricks and Snowflake, translate customer priorities into executable roadmaps, guide engineering teams through delivery, and introduce AI-for-Data capabilities that improve data discovery, engineering productivity, quality, governance and self-service consumption. The architect must combine hands-on platform depth with executive communication and pragmatic thought leadership. Key Accountabilities 1. Customer-Facing Solution Leadership Act as the primary on-ground architecture contact for customer data leaders, enterprise architects, product owners, security teams and engineering leaders. Lead discovery workshops, clarify business outcomes and non-functional requirements, and convert ambiguity into architecture decisions and an executable backlog. Present options, trade-offs, cost implications, risks and recommendations in architecture review boards and executive forums. Maintain decision logs, reference architectures, standards and architecture conformance throughout delivery. Resolve cross-platform and cross-team dependencies while protecting business continuity and production readiness. 2. Databricks & Snowflake Platform Architecture Design target-state lakehouse, warehouse and hybrid architectures using Databricks, Snowflake and cloud-native services. Define ingestion, CDC, streaming, transformation, medallion, dimensional, Data Vault, data product and semantic-layer patterns. Establish platform topology, environment strategy, workload isolation, identity, networking, storage, compute and business-continuity designs. Select fit-for-purpose capabilities across Delta Lake, Unity Catalog, Databricks SQL, MLflow, Delta Sharing, Snowflake virtual warehouses, Snowpark, Dynamic Tables, Snowpipe, Streams, Tasks and secure sharing. Define interoperability patterns using APIs, events, open table formats and governed data sharing without unnecessary duplication or lock-in. 3. AI for Data Initiatives Define and lead AI-enabled data-engineering use cases such as metadata enrichment, mapping suggestions, data-quality anomaly detection, pipeline generation, SQL conversion, documentation and test-case generation. Architect conversational analytics and semantic solutions using Databricks Genie, Snowflake Cortex Analyst or equivalent governed natural-language interfaces. Enable enterprise search and RAG over data catalogs, business glossaries, technical documents and trusted datasets using access-aware retrieval. Design human-review, evaluation, auditability and fallback controls so AI-generated engineering assets remain traceable and safe. Create a roadmap for moving AI-for-Data experiments into reusable accelerators and governed production capabilities. 4. Data Governance, Security & Trust Define governance across catalog, metadata, lineage, business glossary, data ownership, data quality, MDM, retention and data contracts. Establish RBAC/ABAC, row-level security, column masking, encryption, private connectivity, secrets and least-privilege standards. Partner with security, privacy, legal and risk functions on sensitive-data handling, residency, audit and regulatory controls. Define certified metrics and semantic contracts to maintain consistency across platforms and consumption tools. 5. Engineering Standards & Production Readiness Set standards for Python, PySpark, SQL, Snowpark, dbt, notebooks, modular design, testing, observability and supportability. Define CI/CD, infrastructure-as-code, environment promotion, release controls and automated quality gates. Drive performance engineering and FinOps through workload sizing, query optimization, storage design, monitoring, tagging and cost attribution. Approve architecture, technical designs, resilience, recovery, cutover, rollback and operational-readiness plans. 6. Technical Leadership & Delivery Enablement Guide data engineers, platform engineers, modelers, analysts and technical leads through design and implementation. Conduct design reviews, critical code reviews, troubleshooting and technical risk assessments. Support estimations, proposals, SOW inputs, delivery planning, migration waves and change control. Ensure architecture is implemented, tested and adopted rather than remaining a conceptual artifact. 7. Thought Leadership & Practice Contribution Develop customer-specific points of view on lakehouse, data cloud, data products, semantic architecture, AI-ready data and platform modernization. Create reusable reference architectures, decision frameworks, accelerators, assessment tools and implementation playbooks. Facilitate executive briefings, innovation workshops, proofs of value and technology evaluations. Mentor architects and represent the Data & AI practice in customer and partner forums. Mandatory Skills & Experience 16+ years in data engineering, data warehousing or architecture, with ownership of enterprise-scale transformation programs. Deep architecture experience in Databricks and/or Snowflake, with credible working knowledge of both platforms and their trade-offs. Strong knowledge of Spark, SQL, Python, Delta Lake, Snowpark, dbt, streaming, CDC, semantic modeling and cloud architecture. Experience with Unity Catalog or Snowflake Horizon, security, lineage, data quality, MDM and governed data sharing. Hands-on architecture experience with Azure, AWS or Google Cloud, including networking, IAM, secrets and landing-zone integration. Experience leading customer workshops, architecture boards, technical programs and distributed engineering teams. Ability to connect data architecture to AI/ML, GenAI, RAG, agents and conversational analytics requirements. Strong commercial awareness, estimation, risk management and executive communication. Preferred Skills & Certifications Databricks Certified Solutions Architect Professional, SnowPro Advanced: Architect, or relevant cloud architecture certification. Experience with Microsoft Fabric, Power BI, Kafka, Airflow, Fivetran, Informatica, Collibra, Alation or Purview. Experience creating AI-enabled data engineering accelerators or migrating legacy ETL/warehouse estates. TOGAF or equivalent enterprise architecture exposure. Consulting, pre-sales, RFP response and capability-building experience. Core Competencies Competency and their Expected Behavior Architecture ownership Creates coherent target-state designs and remains accountable through implementation and adoption. Platform judgment Selects Databricks, Snowflake or hybrid patterns based on workload, governance, skills, cost and risk. Customer influence Builds trust, simplifies complexity and guides decisions without relying on authority. AI-for-Data thinking Applies AI pragmatically to engineering and governance while preserving human review and traceability. Delivery pragmatism Balances strategic architecture with release constraints, migration risk and operational readiness. Thought leadership Develops relevant points of view and converts them into practical roadmaps and reusable assets. Expected Deliverables Customer-approved target-state architecture, roadmap and architecture decision records. Databricks/Snowflake reference architecture and platform standards. AI-for-Data use-case portfolio, prioritization and productionization roadmap. Security, governance, semantic, quality, FinOps and operational architecture. Migration or modernization waves with coexistence, reconciliation, cutover and rollback patterns. Reusable design templates, implementation patterns and customer-facing thought-leadership assets. Success Measures for the First 12 Months Architecture decisions are approved, implemented and traceable to business and non-functional requirements. Programs show improved scalability, reliability, governance and cost transparency. AI-for-Data initiatives progress from proof of value to controlled, reusable production adoption. Engineering teams consistently use defined platform and delivery standards. Customer stakeholders view the architect as a trusted, outcome-oriented solution leader. Interview Assessment Framework Assessment Area and What to Evaluate Architecture case Design a governed multi-platform architecture and explain workload placement between Databricks and Snowflake. AI-for-Data case Prioritize and architect three AI-enabled data-engineering use cases, including control and evaluation design. Customer workshop Handle conflicting executive, security and engineering priorities and drive a documented decision. Migration case Define discovery, coexistence, reconciliation, cutover, rollback and decommissioning for a legacy estate. FinOps and performance Diagnose an expensive, slow platform and propose evidence-based changes. Thought leadership Present a concise point of view on building an AI-ready data foundation without creating another silo. Suggested Knockout Criteria Cannot show ownership of enterprise data architecture through production implementation. Has depth in only one platform but cannot explain practical Databricks-Snowflake interoperability or workload trade-offs. Cannot connect data architecture to AI, semantic, governance and operational requirements. Produces conceptual diagrams without engineering standards, migration plans or measurable acceptance criteria. Cannot lead senior customer discussions or defend architecture decisions with options and trade-offs. Equal Opportunity Statement We are committed to a fair and inclusive hiring process. Selection decisions should be based on role-relevant capability, experience and demonstrated behaviors. Reasonable accommodations should be provided throughout the recruitment process where required.
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Most IT Jobs posts in Pune ask for 3–6 years of experience (44% of 1149 that say).
From live job posts on CoreHRx, updated 14 Nov 2026. Skill figures count posts that list their skills; salary figures only posts that show a salary.
The post does not state the pay. As an estimate, comparable posts on CoreHRx advertise ₹16L – ₹26L a year (typical for IT Jobs in Pune, 53 posts).
The post asks for 16 Yrs experience.
In Pune (hybrid).
It is a full time job.
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