GCP Data Architect

About The Project
Join Neurons Lab as a
Senior GCP Data Architect
working on
banking data lake and reporting systems
for large financial institutions. This is an end-to-end role where you'll start with presales and architecture - gathering requirements, designing solutions, establishing governance frameworks - then progress to implementing your designs through to MVP delivery.

Our Focus
: Banking and Financial Services clients with stringent regulatory requirements (Basel III, MAS TRM, PCI-DSS, GDPR). You'll architect data lake solutions for critical use cases like AML reporting, KYC data management, and regulatory compliance - ensuring robust data governance, metadata management, and data quality frameworks.

Your Impact
: Design end-to-end data architectures combining
GCP data services
(BigQuery, Dataflow, Data Catalog, Dataplex) with
on-premise systems
(ex. Oracle). Establish data governance frameworks with cataloging, lineage, and quality controls. Then build your designs - implementing data pipelines, governance tooling, and delivering working MVPs for mission-critical banking systems.

Duration:
Part-time long-term engagement with project-based allocations

Reporting:
Direct report to Head of Cloud

Objective

Design and deliver data lake solutions for banking clients on Google Cloud Platform:

• Architecture Excellence: Design data lake architectures, create technical specifications, lead requirements gathering and solution workshops
• MVP Implementation: Build your designs - implement data pipelines, deploy governance frameworks, deliver working MVPs with data quality
• Data Governance: Establish and implement comprehensive governance frameworks including metadata management, data cataloging, data lineage, and data quality standards
• Client Success: Own the full lifecycle from requirements to MVP delivery, ensuring secure, compliant, scalable solutions aligned with banking regulations and GCP best practices
• Knowledge Transfer: Create reusable architectural patterns, data governance blueprints, implementation code, and comprehensive documentation

KPI

• Design data architecture comprehensive documentation and governance framework
• Deliver MVP from architecture to working implementation
• Establish data governance implementations including metadata catalogs, lineage tracking, and quality monitoring
• Achieve 80%+ client acceptance rate on proposed data architectures and technical specifications
• Implement data pipelines with data quality and comprehensive monitoring
• Create reusable architectural patterns and IaC modules for banking data lakes and regulatory reporting systems
• Document solutions aligned with banking regulations (Basel III, MAS TRM, AML/KYC requirements)
• Deliver cost models and ROI calculations for data lake implementations

Areas of Responsibility

Phase 1: Data Architecture & Presales

• Elicit and document requirements for data lake, reporting systems, and analytics platforms
• Design end-to-end data architectures: ingestion patterns, storage strategies, processing pipelines, consumption layers
• Create architecture diagrams, data models (dimensional, data vault), technical specifications, and implementation roadmaps
• Data Governance Design: Design metadata management frameworks, data cataloging strategies, data lineage implementations, data quality monitoring
• Evaluate technology options and recommend optimal GCP and On Premises data services for specific banking use cases
• Calculate ROI, TCO, and cost-benefit analysis for data lake implementations
• Banking Domain: Design solutions for AML reporting, KYC data management, regulatory compliance, risk reporting
• Hybrid Cloud Architecture: Design integration patterns between GCP and on-premise platforms (ex. Oracle, SQL Server)
• Security & compliance architecture: IAM, VPC Service Controls, encryption, data residency, audit logging
• Participate in presales activities: technical presentations, client workshops, demos, proposal support
• Create detailed implementation roadmaps and technical specifications for development teams

Phase 2: MVP Implementation & Delivery

• Build production data pipelines based on approved architectures
• Implement data warehouses: schema creation, partitioning, clustering, optimization, security setup
• Deploy data governance frameworks: Data Catalog configuration, metadata tagging, lineage tracking, quality monitoring
• Develop data ingestion patterns from on-premise systems
• Write production-grade data transformation, validation, and business logic implementation
• Develop Python applications for data processing automation, quality checks, and orchestration
• Build data quality frameworks with validation rules, anomaly detection, and alerting
• Create sample dashboards and reports for business stakeholders
• Implement CI/CD pipelines for data pipeline deployment using Terraform
• Deploy monitoring, logging, and alerting for data pipelines and workloads
• Performance tuning and cost optimization for production data workloads
• Document implementation details, operational runbooks, and knowledge transfer materials

Skills & Knowledge
Certifications & Core Platform:

• GCP Professional Cloud Architect (strong plus, not mandatory) - demonstrates GCP expertise
• GCP Professional Data Engineer (alternative certification)
• Core GCP data services: BigQuery, Dataflow, Pub/Sub, Data Catalog, Dataplex, Dataform, Composer, Cloud Storage, Data Fusion

Must-Have Technical Skills:

• Data Architecture (expert level) - data lakes, lakehouses, data warehouses, modern data architectures
• Data Governance (expert level) - metadata management, data cataloging, data lineage, data quality frameworks, hands-on implementation
• SQL (advanced-expert level) - production-grade queries, complex transformations, window functions, CTEs, query optimization, performance tuning
• Data Modeling (expert level) - dimensional modeling, data vault, entity-relationship, schema design patterns for banking systems
• ETL/ELT Implementation (advanced level) - production data pipelines using Dataflow (Apache Beam), Dataform, Composer, orchestration
• Python (advanced level) - production data applications, pandas/numpy for data processing, automation, scripting, testing
• Data Quality (advanced level) - validation frameworks, monitoring strategies, anomaly detection, automated testing

BFSI Domain Knowledge (MANDATORY):

• Banking data domains: AML (Anti-Money Laundering), KYC (Know Your Customer), regulatory reporting, risk management
• Financial regulations: Basel III, MAS TRM (Monetary Authority of Singapore Technology Risk Management), PCI-DSS, GDPR
• Understanding of banking data flows, reporting requirements, and compliance frameworks
• Experience with banking data models and financial services data architecture

Strong Plus:

• On-premise data platforms: Oracle, SQL Server, Teradata
• Data quality tools: Great Expectations, Soda, dbt tests, custom validation frameworks
• Visualization tools: Looker, Looker Studio, Tableau, Power BI
• Infrastructure as Code: Terraform for GCP data services
• Streaming data processing: Pub/Sub, Dataflow streaming, Kafka integration
• Vector databases and search: Vertex AI Vector Search, Elasticsearch (for GenAI use cases)

Communication:

• Advanced English (written and verbal)
• Client-facing presentations, workshops, and requirement gathering sessions
• Technical documentation and architecture artifacts (diagrams, specifications, data models)
• Stakeholder management and cross-functional collaboration

Experience

• 7+ years in data architecture, data engineering, or solution architecture roles
• 4+ years hands-on with GCP data services (BigQuery, Dataflow, Data Catalog, Dataplex) - production implementations
• 3+ years in data governance (MANDATORY) - metadata management, data lineage, data quality frameworks, data cataloging
• 3+ years in BFSI/Banking domain (MANDATORY) - AML, KYC, regulatory reporting, compliance requirements
• 5+ years with SQL and relational databases - complex query writing, optimization, performance tuning
• 3+ years in data modeling - dimensional modeling, data vault, or other data warehouse methodologies
• 2+ years in presales/architecture roles - requirements gathering, solution design, client presentations
• Experience with on-premise data platforms (MANDATORY) - Ex. Teradata, Oracle, SQL Server integration with cloud