Data Architect - AWS and Snowflake

Aspire Talent Innovations; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Aspire Talent Innovations seeks a senior Data Architect to own the end-to-end data platform—from source systems and CDC through the data lake and Snowflake warehouse to dimensional and semantic models, the BI layer, and emerging AI products.

This role is hands-on and high autonomy, requiring architectural leadership, coding, and governance, with collaboration across CloudOps/DevOps and analytics teams to deliver scalable, compliant data solutions.

Qualifications

• 12–17 years of data engineering/architecture experience with production systems.
• Expert-level SQL, Python, and PySpark able to write, review and debug production code.
• Hands-on AWS and Snowflake architecture with data lifecycle expertise.
• Strong communication and ability to translate requirements into resilient architecture.

Responsibilities

• Design end-to-end data architecture across AWS and Snowflake from raw to curated zones.
• Define standards for data integration, orchestration (Airflow), and warehousing.
• Own data modelling using Data Vault and dimensional models for BI/AI use cases.
• Architect reliable batch and CDC data pipelines; ensure governance and testing.
• Design the semantic layer for Power BI and guide analytics engineers.

Skills

Experience 12-17 years

SQL

Python

PySpark

Data governance

Strong communication

Tools

AWS

Snowflake

Airflow

dbt

AI/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.