About the Role:

We are looking for a Senior Data Architect to lead the architecture, governance, and modernization of enterprise-scale data and analytics platforms.

The role will act as an architecture authority, working closely with business, engineering, cloud, security, governance, and data teams to transform fragmented data environments into scalable, governed, cloud-native and AI-ready platforms.

This is a hands-on architecture leadership role, requiring the ability to define target architectures, establish standards, challenge technical decisions, and guide engineering teams through implementation.

Key Responsibilities:

Enterprise Data Architecture:

- Define enterprise data architecture principles, standards, reference architectures, and target-state platforms.

- Design modern Data Lakehouse / Data Warehouse architectures using Snowflake, Databricks, Azure, AWS, or equivalent technologies.

- Lead modernization of legacy data platforms into scalable cloud-native architectures.

- Define conceptual, logical, and physical data models using Data Vault, Kimball, dimensional modelling, 3NF, or appropriate hybrid approaches.

- Design batch, real-time, streaming, and event-driven data integration architectures.

- Ensure solutions meet requirements for scalability, security, resilience, data quality, compliance, and cost optimization.

Data Products:

- Define and establish a Data Product operating model covering ownership, lifecycle, governance, prioritization, and service expectations.

- Translate business requirements into reusable and governed data products.

- Define product KPIs, quality expectations, adoption metrics, backlogs, and roadmaps.

- Establish accountability for data product quality, documentation, discoverability, access, and consumption.

Data Governance & Quality:

- Establish enterprise data governance frameworks covering data ownership, stewardship, metadata management, cataloguing, lineage, data quality, profiling, and Master Data Management (MDM).

- Define measurable data quality rules, controls, monitoring, and remediation processes.

- Ensure data is trusted, discoverable, traceable, and suitable for analytics and AI/ML.

- Support security, privacy, GDPR, healthcare and other regulatory requirements where applicable.

AI/ML Architecture:

- Provide architecture support for AI/ML use cases.

- Define data preparation, feature engineering, feature-store and model deployment patterns.

- Collaborate with Data Scientists, ML Engineers, and Data Engineers on production AI solutions.

- Support MLOps, model monitoring, drift detection, and responsible AI governance.

Platform & Engineering Governance:

- Provide architecture leadership across Snowflake, Databricks, Azure and AWS data platforms.

- Define reusable engineering patterns, standards, guardrails, and architecture review processes.

- Review technical designs for scalability, security, resilience, maintainability, cost, and technical debt.

- Mentor architects, data engineers, AI engineers, and delivery teams.

- Support technology evaluation, vendor assessment, architecture risk reviews, and roadmap planning.

Stakeholder & Executive Engagement:

- Lead architecture and discovery workshops with business and technology stakeholders.

- Translate business objectives into data capabilities, KPIs, and technology roadmaps.

- Present architecture options, trade-offs, risks, costs, and recommendations to senior stakeholders.

- Challenge unclear or overly complex requirements and drive pragmatic architecture decisions.

Mandatory Skills & Experience:

- 15+ years of experience, with significant experience in Enterprise Data Architecture.

- 5+ years of experience with cloud-native data platforms.

- Strong architecture experience with Snowflake and Databricks.

- Strong experience with Azure and AWS data services.

- Hands-on experience designing Data Lakehouse / Data Warehouse architectures.

- Strong knowledge of Data Vault, Kimball, dimensional modelling, conceptual, logical and physical data modelling.

- Strong Data Governance experience covering metadata, lineage, cataloguing, ownership, stewardship, data quality and MDM.

- Proven experience with Data Products / Data Product Management / Data Mesh.

- Strong SQL, Python, PySpark and ETL/ELT experience.

- Experience with Kafka, streaming, CDC or equivalent integration patterns.

- Working knowledge of AI/ML architecture, feature engineering, MLOps and model lifecycle.

- Strong stakeholder management and executive communication skills.

- Proven experience leading architecture decisions and guiding engineering implementation.

Preferred Skills:

- Collibra, Microsoft Purview, Databricks Unity Catalog, Azure Data Factory, Azure Data Lake Storage, Azure Synapse, AWS Glue / S3 / Redshift, Kafka, Spark / PySpark, MDM, Data Quality frameworks, Data Mesh / Data Product architecture, Healthcare / BFSI / regulated-industry experience.

What We Are Looking For:

- We are looking for an architect who can own the architecture from strategy through implementation - not someone limited to documentation or advisory work.

- Define, Challenge, Govern, Guide, Deliver enterprise data architecture while working effectively with senior business and technology stakeholders.

- Immediate availability and willingness to work from Electronic City, Bangalore 3 days per week is preferred.