Senior Data Architect /Lead

As Senior Data Architect / Lead, you'll define how data is structured and ensure it flows seamlessly from source to production ML. This role blends architecture, modeling, and ML enablement. You'll design the data foundations for model development and deployment — working with Data Science and Platform teams to ship models safely — but won't own 24/7 infra or MLOps tooling.

What You'll Own

• Data Architecture & Strategy: Define enterprise data architecture for analytics, BI, and ML use cases. Set the roadmap for real-time + batch data
• Model Development Enablement: Architect feature pipelines, training data layers, and semantic models that accelerate DS teams. Own feature store design and data contracts for ML
• Model Deployment Design: Partner with ML & Platform Engineering to architect deployment patterns for batch and real-time inference. Define standards for model data inputs, monitoring data, and rollback
• Data Modeling: Lead design of canonical models and warehouses that serve both analysts and production models
• Data Governance for ML: Establish lineage, auditability, and quality checks for training + inference data. Ensure PDPA/GDPR compliance for model features
• Platform Guardrails: Define requirements for serving layers, online stores, and monitoring — but Platform Engineering builds/runs them
• Stakeholder Leadership: Bridge Product, Analytics, Data Science, and Engineering to deliver data + ML solutions
• Team Enablement: Mentor engineers on building production-grade data products. Review designs for model-facing datasets

What You'll Need

• Experience: 10+ years in data, with 3+ years as a Data Architect, Lead Data Engineer, or ML-adjacent architect
• Data Modeling Mastery: Expert in dimensional modeling, feature engineering, and designing for both BI + ML consumption
• Model Lifecycle Exposure: Hands-on experience across model dev and deployment — you've shipped features to prod, defined inference schemas, or designed monitoring datasets. Understand batch vs online serving tradeoffs
• SQL & Warehousing: Deep expertise in BigQuery, Snowflake, Redshift, or Databricks
• Architecture Skills: Designed large-scale platforms supporting training, batch inference, and real-time scoring
• Tech Fluency: Strong SQL + Python. Can read/write dbt. Understand Airflow, Spark, feature stores, and model registry concepts
• Communication: Can align DS, Eng, and Product on data contracts and deployment standards