Google Cloud Data Architect – IAM Data Modernization

Project/Program

Identity & Access Management (IAM) Data Modernization – migration of an on‑premises SQL data warehouse to a target‑state Data Lake on Google Cloud (Google Cloud Platform), enabling metrics & reporting, advanced analytics, and GenAI use cases (natural language querying, accelerated summarization, cross‑domain trend analysis).

About Program/Project

The IAM Data Modernization project involves migrating an on-premises SQL data warehouse to a target state Data Lake in  Google Cloud Platform cloud environment. Key highlights include:

• Integration Scope: 30+ source system data ingestions and multiple downstream integrations
• Capabilities: Metrics, reporting, and Gen AI use cases with natural language querying, advanced pattern/trend analysis, faster summarizations, and cross-domain metric monitoring
• Benefits:
• Scalability and access to advanced cloud functionality
• Highly available and performant semantic layer with historical data support
• Unified data strategy for executive reporting, analytics, and Gen AI across cyber domains

This modernization establishes a single source of truth for enterprise-wide data-driven decision-making.

Required Skills

Data Lake Architecture & Storage

• Proven experience designing and implementing data lake architectures (e.g., Bronze/Silver/Gold or layered models).
• Strong knowledge of Cloud Storage (GCS) design, including bucket layout, naming conventions, lifecycle policies, and access controls

·       Experience with Hadoop/HDFS architecture, distributed file systems, and data locality principles

• Hands-on experience with columnar data formats (Parquet, Avro, ORC) and compression techniques
• Expertise in partitioning strategies, backfills, and large-scale data organization
• Ability to design data models optimized for analytics and BI consumption

Qualifications

• Experience: [10–14]+ years in data engineering/architecture, 5+ years designing on Google Cloud Platform at scale; prior on‑prem → cloud migration a must.
• Education: Bachelor’s/Master’s in Computer Science, Information Systems, or equivalent experience.
• Certifications: Google Cloud Professional Cloud Architect (required or within 3 months). Plus: Professional Data Engineer, Security Engineer.

Data Ingestion & Orchestration

·       Experience building batch and streaming ingestion pipelines using Google Cloud Platform-native services

·       Knowledge of Pub/Sub-based streaming architectures, event schema design, and versioning

·       Strong understanding of incremental ingestion and CDC patterns, including idempotency and deduplication

·       Hands-on experience with workflow orchestration tools (Cloud Composer / Airflow)

·       Ability to design robust error handling, replay, and backfill mechanisms

Data Processing & Transformation

·       Experience developing scalable batch and streaming pipelines using Dataflow (Apache Beam) and/or Spark (Dataproc)

·       Strong proficiency in BigQuery SQL, including query optimization, partitioning, clustering, and cost control.

·       Hands-on experience with Hadoop MapReduce and ecosystem tools (Hive, Pig, Sqoop)

·       Advanced Python programming skills for data engineering, including testing and maintainable code design

·       Experience managing schema evolution while minimizing downstream impact

Analytics & Data Serving

·       Expertise in BigQuery performance optimization and data serving patterns

·       Experience building semantic layers and governed metrics for consistent analytics

·       Familiarity with BI integration, access controls, and dashboard standards

·       Understanding of data exposure patterns via views, APIs, or curated datasets

Data Governance, Quality & Metadata

·       Experience implementing data catalogs, metadata management, and ownership models

·       Understanding of data lineage for auditability and troubleshooting

·       Strong focus on data quality frameworks, including validation, freshness checks, and alerting

·       Experience defining and enforcing data contracts, schemas, and SLAs

·       Familiarity with audit logging and compliance readiness

Cloud Platform Management

·       Strong hands-on experience with Google Cloud Platform (Google Cloud Platform), including project setup, environment separation, billing, quotas, and cost controls

·       Expertise in IAM and security best practices, including least-privilege access, service accounts, and role-based access

·       Solid understanding of VPC networking, private access patterns, and secure service connectivity

·       Experience with encryption and key management (KMS, CMEK) and security auditing

DevOps, Platform & Reliability

·       Proven ability to build CI/CD pipelines for data and infrastructure workloads

·       Experience managing secrets securely using Google Cloud Platform Secret Manager

·       Ownership of observability, SLOs, dashboards, alerts, and runbooks

·       Proficiency in logging, monitoring, and alerting for data pipelines and platform reliability

Good to have

Security, Privacy & Compliance

·       Hands-on experience implementing fine-grained access controls for BigQuery and GCS

·       Experience with VPC Service Controls and data exfiltration prevention

·       Knowledge of PII handling, data masking, tokenization, and audit requirements