GCP Data Architect

GCP Data Architect Apply for the

GCP Data Architect

role at

Neurons Lab .

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

(e.g., 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

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 (e.g., 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) — e.g., Teradata, Oracle, SQL Server integration with cloud.

Seniority level Mid‑Senior level

Employment type Part-time

Job function Engineering and Information Technology

Industries IT Services and IT Consulting

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