Data Architect - AWS and Snowflake

Aspire Talent Innovations; India; Remote; INR 4,000,000 - 8,000,000; Full time

Job summary

Aspire Talent Innovations is seeking a hands-on Data Architect to own the design of our data platform from source systems through the data lake and Snowflake warehouse to BI and AI products.

You will design and govern data integration, modelling, and the semantic layer, collaborating with data engineers, analysts, and AI teams. Strong leadership and coding skills are essential for this senior role.

Qualifications

• 12-17 years of data engineering/architecture experience with hands-on production systems.
• Expert AWS (S3/IAM) and Snowflake know-how including RBAC and cost controls.
• Hands-on SQL, Python, and PySpark in production code and reviews.
• Experience with Airflow or MWAA and dbt or equivalents for modern pipelines.
• Ability to translate ambiguous business needs into resilient data architectures.

Responsibilities

• Own end-to-end data architecture from sources to semantic models and AI products.
• Design and govern data integration, pipelines, and data warehousing standards.
• Define conformed dimensions, data contracts, and reusable data products.
• Architect reliable batch and CDC-based data pipelines into lake and warehouse.
• Guide BI/analytics teams on semantic layer and metric definitions.
• Collaborate with AI teams on feature pipelines and MLOps touchpoints.
• Lead governance, data quality, and architecture decisions with senior staff.

Skills

Data architecture

AWS

Snowflake

Python

PySpark

Airflow

dbt

Data Vault

Kimball modelling

Dimensional modelling

Tools

GitHub Copilot

Cursor

Claude Code

ChatGPT/LLM tooling

Job description

About the Role

We're hiring a hands-on Data Architect to own the end-to-end design of our data platform from sourcesystem integration and CDC, through the data lake and Snowflake warehouse, into dimensional and semantic models, the BI layer, and our emerging AI products. This is a senior, high-autonomy role for someone who has architected and scaled modern data platforms before, who still codes and reviews architecture hands-on rather than only diagramming it, and who is comfortable using AI-assisted development tools to move faster without cutting corners on quality or governance.

What You'll Do

• Architecture & Platform Design
• Design and own the end-to-end data architecture across AWS (S3, IAM, and core data services) and Snowflake from raw/bronze through conformed/silver to curated/gold zones partnering with our CloudOps/DevOps team on networking, provisioning, and infrastructure operations.
• Define the standards and patterns for data integration, pipeline orchestration (Airflow/MWAA), and data warehousing that the wider engineering team builds against.
• Evaluate and select tools, patterns, and vendors as the platform scales, balancing cost, performance, and maintainability.Data Modelling & Warehousing
• Own conceptual, logical, and physical data modelling across core business domains using Data Vault for the historised, audit-ready layer and dimensional (star schema) models for consumption.
• Define conformed dimensions, data contracts, and reusable data products consumed by BI, analytics, data science, and AI teams.Data Integration & Pipelines
• Architect reliable batch and CDC-based data integration pipelines from core operational systems into the lake and warehouse.
• Partner with data engineers on pipeline reliability, automated testing, schema-drift handling, and SLA design.BI & Semantic Layer
• Design the semantic layer and certified metric definitions that power Power BI and self-serve analytics one trusted definition per business metric, everywhere it's used.
• Guide BI developers and analytics engineers on performant, well-governed dataset design.AI Enablement
• Architect the data foundations governed marts, semantic layer, retrieval/vector infrastructure that ground the organisation's AI products, including a business-facing GenAI chatbot.
• Partner with data science and AI engineering on feature pipelines, model-serving data needs, and MLOps/LLMOps integration points.AI-Augmented Engineering
• Actively use AI development tools (e.g., GitHub Copilot, Cursor, Claude Code, or similar) to accelerate design, coding, testing, documentation, and repetitive engineering work.
• Champion practical AI-assisted workflows across the data organisation code generation, pipeline scaffolding, test generation, documentation, code review while holding the line on engineering rigor.Leadership & Governance
• Set technical direction and review architecture and design decisions across data engineering, analytics engineering, and BI.
• Partner with the Director of Data on governance and data-quality standards and the technical roadmap; mentor senior engineers.

What You'll Need

• 12-17 years of overall data engineering/architecture experience, including demonstrable experience architecting production systems not only diagramming them.
• You're expected to own AWS architecture and design decisions, not perform hands-on networking work yourself our CloudOps/DevOps team implements and operates that layer.
• Expert-level experience with AWS (S3, IAM, and core data services) and Snowflake (architecture,performance tuning, cost optimisation, RBAC).
• Hands-on experience across the full data lifecycle: data integration/CDC, data pipeline engineering, data warehousing, dimensional modelling (Kimball) and/or Data Vault, semantic/BI layer design, and exposure to AI/ML-driven data products.
• Expert-level SQL, Python, and PySpark able to write, review, and debug production code, not just specify it.
• Practical, hands-on experience using AI development tools (e.g., GitHub Copilot, Cursor, Claude Code,ChatGPT/LLM-based tooling) for coding, automation, documentation, and other development workflows, with a clear point of view on where they help and where they don't.
• Experience with workflow orchestration (Airflow or equivalent) and modern transformation tooling (dbt or equivalent).
• Strong communication skills able to work directly with business stakeholders and translate ambiguous requirements into resilient architecture.

Nice to Have

• Experience in financial services, lending, collections, or debt-settlement/consumer-finance environments,including awareness of relevant regulatory considerations (e.g., FDCPA, TCPA, CFPB-adjacent data handling).
• Experience standing up or scaling a data organisation from a small team, including defining architecture standards from scratch.
• Experience with GenAI/RAG architectures, vector search, and LLMOps tooling.
• Relevant certifications (AWS, SnowPro).

Location - Pan india, Remote.