Job Description

About the Engagement

15-month enterprise Data & AI transformation program focused on building a secure, governed, and AI-ready lakehouse ecosystem on Google Cloud. The engagement includes migration of legacy data assets (Snowflake, Databricks, existing BigQuery environments, data pipelines, and ML workloads) with zero business disruption, while establishing the operating model, capabilities, and controls required for long-term success.

Delivery follows a three-in-a-box model involving the client, Google Cloud, and the delivery partner, across the following workstreams:

• Platform Foundation
• Migration & Cutover
• Data & AI Governance
• Change Management & Enablement
• Value Realization & FinOps
• Process Reengineering

Role Structure

Senior Individual Contributor with architectural authority:

• ~40% Architecture – target-state and migration design, Architecture Decision Records (ADRs), participation in Design Authority.
• ~40% Delivery – hands-on implementation, migration factory execution, cutover validation.
• ~20% Change Management & Adoption – working sessions with client business and engineering teams.

Responsibilities

Architecture

• Design components of the governed lakehouse foundation, including:
  • BigQuery compute
  • Apache Iceberg storage
  • Batch, streaming, and CDC ingestion
  • dbt-based transformations with CI/CD
  • Data catalog
  • Lineage
  • Data quality frameworks
  • Semantic layer
• Produce Architecture Decision Records (ADRs) and defend them within Design Authority and Architecture Review Board (ARB) processes, covering security, cost management, and hard-stop data risk controls.
• Assess in-scope Snowflake and Databricks workloads and define migration strategies (rehost, re-platform, or re-architect) based on the principle of migrate logic, not debt.
• Contribute to the AI and agentic control-plane architecture, including LLM gateways, tool/MCP registries, evaluation frameworks, and human-in-the-loop capabilities where they intersect with the data platform.
• Uphold the following non-negotiable principles:
  • Zero downtime
  • Trusted data
  • No partial cutovers
  • Security findings treated as hard stops
  • Cost management as a first-class deliverable
  • Dual-run costs capped at 120% of baseline

Hands-On Delivery

• Execute within the migration factory, including:
  • Data pipelines
  • Data products
  • Parallel-run validation for data, performance, cost, and quality
  • Progressing workloads through cutover gates to legacy decommissioning
• Implement federated governance as code, including:
  • Data contracts
  • Data quality rules
  • Lineage
  • Access controls
  • Certification workflows
• Support wave planning activities:
  • Workload inventory
  • Dependency mapping
  • Effort estimation
  • Sequencing based on business value and risk
• Troubleshoot production-impacting issues during dual-run and hypercare phases.
• Develop operational runbooks and support build-operate-transfer handovers.

Change Management & Stakeholder Engagement

• Translate platform changes for business teams, including:
  • New tools
  • New ownership models (data-as-a-product, domain stewardship)
  • Hub-and-spoke operating models
• Facilitate stakeholder working sessions, including:
  • Requirements walkthroughs
  • Disposition reviews
  • Adoption clinics
  • Objection handling
• Coach client engineers through pairing and enablement activities.
• Deliver measurable capability transfer.
• Proactively identify adoption risks and escalate them to Program Leadership with recommended mitigation actions.

Mandatory Requirements

• 10+ years of experience in Data Engineering and/or Data Architecture.
• Minimum 3 years of client-facing consulting or professional services experience.
• Deep hands-on expertise across the Google Cloud data stack, including:
  • BigQuery (data modeling, performance optimization, cost optimization)
  • Dataproc
  • Dataflow or equivalent streaming/CDC technologies
  • dbt
  • Composer/Airflow orchestration
  • Data-focused CI/CD practices
• Proven experience delivering at least one large-scale platform migration involving Snowflake, Databricks, or on-premises/cloud platforms to a lakehouse architecture, including parallel-run validation and production cutover.
• Experience with open table formats, preferably Apache Iceberg, and modern lakehouse architecture patterns.
• Practical implementation experience in data governance, including:
  • Data catalogs
  • Lineage
  • Data quality frameworks
  • Data contracts
  • Access management
  • Federated governance models
  • Data mesh and hub-and-spoke architectures
• Demonstrated ability to lead stakeholder sessions, workshops, design reviews, and complex transformation discussions.
• Experience with structured change management frameworks is advantageous (e.g., ADKAR).

Preferred Qualifications

• Telecommunications industry experience, including:
  • Network data
  • CDR/usage data
  • Customer 360 platforms
  • CLM/campaign data
• Knowledge of Snowflake and/or Databricks internals, including:
  • Spark
  • Delta Lake
  • Snowflake SQL
  • Tasks
  • Streams
• Experience with GenAI and agentic patterns on data platforms, including:
  • Semantic layers for LLM consumption
  • Gemini / Vertex AI
  • MCP and tool integrations
  • RAG over governed enterprise data
• FinOps experience, including:
  • Cost baselining
  • Cost allocation and tagging
  • Forecasting
  • Cost-per-workload modeling
• MLOps and DataOps experience, including feature pipelines and model CI/CD.
• Google Cloud Professional Data Engineer and/or Professional Cloud Architect certification.
• Experience working in the Philippines market or with Southeast Asian enterprise customers.

Success in the First 90 Days

• Become a trusted technical counterpart for at least one client workstream lead, with ADRs accepted by the Design Authority.
• Deliver hands-on contributions to Wave 1 disposition and pilot build activities, including at least one workload successfully validated through parallel run.
• Independently lead enablement and adoption sessions, achieving positive stakeholder feedback and documented capability transfer.