Google Cloud Data Architect – IAM Data Modernization

Location : Dallas TX / Charlotte NC (Hybrid 3 days office)

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 (GCP) enabling metrics & reporting advanced analytics and GenAI use cases (natural language querying accelerated summarization cross domain trend analysis) leveraging PySpark based processing cloud native DevOps CI/CD pipelines and containerized deployments on OpenShift (OCP) to deliver scalable secure and high performance data solutions.

About Program/Project

The IAM Data Modernization project involves migrating an on-premises SQL data warehouse to a target state Data Lake in GCP 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

DevOps / CI CD

• Experience implementing CI/CD pipelines for data and analytics workloads
• Familiarity with Git based source control build automation and deployment strategies

Containers & Platform

• Experience with OpenShift Container Platform (OCP) for deploying data workloads and services
• Understanding of containerized architecture scaling and environment management
• Proven ability to build CI/CD pipelines for data and infrastructure workloads
• Experience managing secrets securely using GCP Secret Manager
• Ownership of observability SLOs dashboards alerts and runbooks
• Proficiency in logging monitoring and alerting for data pipelines and platform reliability

Big Data & Processing

• Hands on experience with PySpark for ETL/ELT data transformation and performance optimization
• Solid understanding of distributed data processing concepts

Data & Cloud Architecture

• Strong experience designing data platforms on Google Cloud Platform (GCP)
• Experience with Data Lakes data warehousing and large scale migration programs

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

Data Ingestion & Orchestration

Experience building batch and streaming ingestion pipelines using GCP-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