Senior Data Consultant

MindSolve Consulting; Manila; On-site; PHP 2,400,000 - 3,600,000; Full time

Job summary

MindSolve Consulting is seeking an senior data engineer to lead a lakehouse migration from Snowflake and Databricks to Google Cloud Platform. Based in the Philippines, the role requires extensive hands-on experience with the GCP data stack and governance patterns.

The ideal candidate has 10+ years in data engineering/architecture, 3+ years in client-facing consulting, and a track record of large-scale migrations with production cutover. Strong SI/GAI integration experience is a plus.

Qualifications

• 10+ years of data engineering / data architecture experience.
• 3+ years of client-facing consulting or professional services.
• At least one large-scale platform migration with parallel-run validation and production cutover.

Responsibilities

• Lead a large lakehouse migration from Snowflake and Databricks to GCP.
• Hands-on work with GCP data stack: BigQuery modeling/performance/cost, Dataproc, Dataflow.
• Implement dbt transformations with CI/CD; orchestrate with Airflow.
• Establish open table formats (Iceberg) and lakehouse patterns; govern data catalogs, lineage, and data contracts.
• Lead stakeholder sessions: design reviews, workshops; ADRs with security, cost, and risk controls.

Skills

10+ years data engineering

data architecture

client-facing consulting

large-scale migrations

GCP data stack

dbt CI/CD

Airflow

Iceberg/Open table formats

data governance

stakeholder facilitation

Tools

BigQuery

Dataproc

Dataflow

dbt

Airflow

Apache Iceberg

Job description

Drive a large lakehouse migration from Snowflake and Databricks to GCP.

Drive a large lakehouse migration from Snowflake and Databricks to GCP.

Based in the Philippines

10+ years data engineering / data architecture

3+ years client-facing consulting or professional services

At least one large-scale platform migration with parallel-run validation and production cutover — Snowflake, Databricks or on-prem to cloud lakehouse

Deep hands-on GCP data stack: BigQuery modeling/performance/cost, Dataproc, Dataflow or equivalent streaming/CDC

dbt transformation with CI/CD

Orchestration: Composer / Airflow

Open table formats (Apache Iceberg preferred) and lakehouse architecture patterns

Practical data governance: catalogs, lineage, data quality frameworks, data contracts, access management

Federated / data-mesh / hub-and-spoke governance models

Stakeholder session leadership: workshops, design reviews, difficult transformation conversations

ADRs defended at Design Authority / ARB with security, cost and risk hard stops

Zero downtime, no partial cutover, dual-run cost capped at 120% of baseline

Wave planning: workload inventory, dependency mapping, effort estimation

Telecom domain (network data, CDR/usage, customer 360)

Snowflake and/or Databricks internals (Spark, Delta, Snowflake SQL/tasks/streams)

GenAI/agentic patterns on data platforms: semantic layers for LLM, Gemini/Vertex AI, MCP/tool integration, RAG over governed data

FinOps: cost baselining, allocation/tagging, forecast

MLOps/DataOps (feature pipelines, model CI/CD)

Google Cloud Professional Data Engineer and/or Professional Cloud Architect — Preferred