Data Architect

10+ years in data architecture | 3+ years leading teams | Multi-cloud expertise | AI/ML-aware

About the role

Lead enterprise data architecture, manage a team of data engineers/analysts, and run data operations — while ensuring the platform is governance-compliant and AI-ready.

Key responsibilities

Architecture — Design and own the enterprise data strategy, models, and integration patterns (batch, streaming, CDC, APIs) across data lakes, warehouses, and lakehouse architectures.

Cloud — Architect on AWS, Azure, and/or GCP. Optimize for cost, resilience, and scale using IaC (Terraform, CloudFormation, Bicep).

Governance — Enforce data quality, lineage, cataloging, classification, access controls, and compliance (GDPR, CCPA, HIPAA, SOX, PCI-DSS, EU AI Act).

AI/ML Readiness — Understand the ML lifecycle, feature stores, MLOps, LLMs, RAG, and vector databases. Architect platforms that data science teams can self-serve. You don't build models — you build the data foundation they run on.

Team — Hire, mentor, and grow a high-performing data team. Set goals, run reviews, drive collaboration across engineering, data science, product, and business.

Operations — Own pipeline reliability (99.5%+ uptime), SLAs, incident response, observability, DataOps, and vendor management.

Required Skills & Qualifications:

• 10+ years of experience in data architecture, with 3+ years successfully leading and mentoring high-performing teams with cross-functional influence
• Expert SQL & data modeling (Kimball, Data Vault, 3NF)
• Cloud platforms (AWS / Azure / GCP) — hands-on
• Data governance & regulatory compliance — proven track record
• AI/ML ecosystem — strong conceptual understanding
• Agile delivery experience

Good To Have:

• Cloud / data certifications (AWS SA, Azure DE, GCP PDE, Snowflake, CDMP)
• Data mesh / data fabric experience
• Regulated industry background (finance, healthcare, pharma)
• Exposure to GenAI data infrastructure (RAG, vector DBs, embeddings)