Senior Data and AI Consultant – GCP Data Analytics & Modernization

Senior Data and AI Consultant – GCP Data Analytics & Modernization: Data Analytics Professional Services

Location: India (Bengaluru, Hyderabad, Pune, Gurugram, or Remote / Hybrid across India; occasional travel for client workshops as needed)
Duration: 6 months, with potential of extension
Work Model: Hybrid / Remote (India Standard Time with overlap for global stakeholder collaboration)


Engagement Overview:
We are looking for a hands-on Senior Data and AI Consultant to drive enterprise-grade Data Analytics and Cloud Modernization initiatives on Google Cloud Platform (GCP). Ideal resources should have a strong understanding of AI agentic applications and Data readiness requirements to enable these.

In this role, you will bridge the gap between technical architecture, engineering execution, and business stakeholder enablement. You will help modernise legacy data estates, construct scalable analytical lakehouse architectures on GCP, and establish governed, production-ready data pipelines and data products.


Anticipated Role Distribution
● ~40% Architecture & Solution Design: Target-state architecture, migration patterns, and technical governance (Architecture Decision Records / Design Authorities).
● ~40% Hands-On Engineering & Delivery: Data pipeline engineering, analytical modelling, cutover validation, and operational hardening.
● ~20% Enablement & Stakeholder Adoption: Running technical working sessions, coaching client engineering teams, and driving operating model adoption.


Key Responsibilities
1. Architecture & Lakehouse Foundation and governance
● Architect and scale enterprise data foundations on GCP with AI agentic applications and use cases in mind: BigQuery compute/storage, data lake design (Cloud Storage, Apache Iceberg/Delta Lake), batch/streaming ingestion, and semantic layers.
● Create comprehensive Architecture Decision Records (ADRs) and present solutions to customer architecture review boards (ARBs), balancing security, scalability, and FinOps cost controls.
● Evaluate existing legacy architectures (Cloudera, Snowflake, Databricks, Teradata, SQL Server, or on-prem platforms) and establish modernization blueprints focused on modern data modelling rather than lifting-and-shifting technical debt.
● Incorporate modern AI/analytics readiness and governance into the platform design (e.g., Vertex AI integration, governed semantic layers for BI/AI consumption).


2. Hands-On Delivery & Pipeline Modernization
● Build and orchestrate robust, production-grade ELT/ETL pipelines using BigQuery, dbt, Cloud Composer (Airflow), Dataflow, or Dataproc.
● Implement parallel-run and automated data validation frameworks (covering schema, record counts, financial/business metrics, and latency) to ensure zero business interruption during transitions.
● Implement data governance and quality mechanics directly in code: automated data contracts, data quality checks, lineage, and access controls (e.g., Dataplex, Collibra, or open-source equivalents).
● Resolve critical issues during cutover, dual-run, and hypercare periods while authoring detailed runbooks and standard operating procedures (SOPs).


3. Stakeholder Engagement, Adoption & Enablement
● Partner with client business domains and analytics teams to translate platform capabilities into actionable analytics solutions and self-serve capabilities. ● Facilitate stakeholder workshops, data discovery sessions, and technical clinics to drive adoption across cross-functional teams.
● Mentor and pair with client data engineers and analytics engineers to ensure sustainable knowledge transfer and capability building.
● Actively identify delivery bottlenecks and operational risks, providing structured technical mitigations.


Required Qualifications & Skills
● Experience: 8–12+ years of total experience in Data Engineering / Data Architecture, with at least 3+ years in client-facing consulting, professional services, or global capability centers (GCCs).
● Core GCP Mastery: Deep, hands-on experience across the GCP Data Stack:
○ BigQuery: Advanced SQL, partitioning/clustering, performance tuning, query optimization, and cost governance.
○ Orchestration & Transformation: dbt (Core or Cloud), Apache Airflow / Cloud Composer.
○ Processing/Compute: Dataflow (Apache Beam), Dataproc (Spark/PySpark), or Cloud Functions/Run.
● Data Modernization & Migration: Proven track record of executing enterprise-scale data migration or analytics platform rebuilds (from Cloudera, Databricks, Teradata, Hadoop, or on-prem DWHs to GCP).
● Data Modeling & Architecture: Strong foundation in dimensional modeling (Kimball), Data Vault, or Lakehouse paradigms (Apache Iceberg, Delta Lake).
● Data Governance & Quality: Hands-on experience embedding data governance (metadata cataloging, lineage tracking, row/column-level security, data contracts).
● Communication & Collaboration: Excellent verbal and written English communication skills; confident interfacing directly with technical leads, enterprise architects, and senior leadership.


Preferred Qualifications
● Certifications: Google Cloud Certified Professional Data Engineer or Google Cloud Certified Professional Cloud Architect.
● Cross-Platform Knowledge: Familiarity with Cloudera, Snowflake (Tasks, Streams, Snowpipe) and/or Databricks (Unity Catalog, Spark internals).
● AI & Modern Analytics: Exposure to Generative AI data enablement, Vertex AI, RAG architectures, or vector search on GCP.
● Agentic Development & GenAI Architecture: Exposure to developing agentic workflows, autonomous tool-use patterns, and multi-agent systems interacting with structured data; experience integrating LLM gateways, Model Context Protocol (MCP) or tool registries, function calling, Retrieval-Augmented Generation (RAG) over governed data, and GCP AI services (Vertex AI, Gemini models).
● DataOps & CI/CD: Hands-on practice with Git workflows, automated testing frameworks, and CI/CD pipelines for data (GitHub Actions, GitLab CI, or Cloud Build).
● FinOps: Understanding of cloud cost monitoring, workload-based cost allocation, and resource reservation management