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Open nowFirst seen 31 hours ago

ML Ops

newbridge101 open roles

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This job: first seen 31 hours ago

The posting

Job Title: Data Governance & MLOps Manager

Role Overview

Own the intersection of Data Governance and MLOps. You will build trusted data and trusted AI - ensuring our clients data is accurate, compliant, and governed, and our ML models are reproducible, monitored, and responsibly deployed to production.

This role is 50% Data Governance, 50% MLOps / ML Platform Governance.

Key Responsibilities

A. Data Governance (50%)

  1. Framework & StewardshipDesign and run enterprise Data Governance framework, policies, and RACI for data owners/stewards Establish Data Governance Council and operating model across Product, Engineering, Analytics, and Business Define KPIs: catalog coverage, data quality score, policy adherence
  2. Data Quality, Catalog & LineageImplement business glossary, data catalog (Collibra / Alation / Purview / DataHub), and end-to-end lineage Define and monitor data quality rules, SLAs, anomaly detection for critical domains (Customer, Product, Transaction) Manage data classification, PII/PHI tagging, retention, and access control policies
  3. Compliance & SecurityEnsure compliance with PDPA, GDPR, CCPA and internal security standards Partner with DPO / Legal / GRC for consent, purpose limitation, anonymization, and audit readiness Own access governance - RBAC/ABAC for data warehouse, lakehouse, and feature store

B. MLOps & AI Governance (50%)

  1. ML Lifecycle & PlatformOwn MLOps best practices: from feature engineering -> training -> validation -> deployment -> monitoring Build and manage ML platform components: Feature Store (Feast / Tecton / SageMaker Feature Store), Model Registry (MLflow / SageMaker Model Registry), Experiment Tracking Standardize CI/CD/CT for ML with Git, Docker, Airflow / Kubeflow / SageMaker Pipelines
  2. Model Governance & Responsible AIImplement Model Governance: model inventory, model cards, lineage (data -> features -> model -> endpoint), approval workflows Enforce responsible AI checks: bias/fairness, explainability, drift, and reproducibility Align with AI Governance frameworks: NIST AI RMF, Singapore Model AI Governance Framework, AI Verify, ISO 42001
  3. Monitoring & OperationsImplement monitoring for data drift, concept drift, feature skew, and model performance degradation Set up alerting, automated retraining triggers, and rollback strategies Optimize model serving costs, latency, and scalability on AWS / Azure / GCP

Tech Stack You Will Work With

Governance: Collibra, Alation, Purview, Informatica, DataHub, AWS Glue, Apache Atlas Data: Snowflake / BigQuery / Redshift, S3 / GCS, dbt, Airflow, Spark, Kafka MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Feast, Evidently, Great Expectations, Docker, Kubernetes, GitHub Actions Languages: Python (must), SQL (must), PySpark

Requirements

  • 6-10 years total in Data Engineering / Data Governance / MLOps
  • At least 2+ years owning data governance and at least 2+ years deploying ML models to production
  • Strong hands-on with DAMA-DMBOK and MLOps principles
  • Proven experience setting up Model Registry, Feature Store, and monitoring for production ML systems
  • Deep understanding of PDPA/GDPR, data security, and AI risk
  • Excellent stakeholder management - you can talk to both Data Scientists and Risk/Legal

Nice-to-Have

  • CDMP, AWS Certified ML Specialty, or similar
  • Experience with LLM / GenAI governance - prompt logging, RAG governance, hallucination monitoring
  • Experience with Great Expectations, Monte Carlo, Evidently AI
  • Industry experience in Media, FinTech, or other regulated industry

What Success Looks Like in 12 Months

  • Top 5 data domains governed with SLAs and quality monitoring >95%
  • 100% of production models registered with model cards, lineage, and approval workflow
  • Automated drift detection live for all critical models with <2hr alert SLA
  • Data catalog adoption >80% and zero compliance audit findings
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