Google Cloud Data Architect – IAM Data Modernization

OMG Technology•Matthews, NC

2d•Hybrid

About The Position

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, and a 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.

Requirements

Experience implementing CI/CD pipelines for data and analytics workloads.
Familiarity with Git-based source control, build automation, and deployment strategies.
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.
Hands-on experience with PySpark for ETL/ELT, data transformation, and performance optimization.
Solid understanding of distributed data processing concepts.
Strong experience designing data platforms on Google Cloud Platform (GCP).
Experience with Data Lakes, data warehousing, and large-scale migration programs.
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.
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).
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.
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.
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.
Hands-on experience implementing fine-grained access controls for BigQuery and GCS.
Experience with Sprint planning and helping team technically.
Strong stakeholder communication and solution-architecture skills.
10–14+ years in DevOps and Data Architecture.
5+ years designing on Pyspark/GCP/OCP at scale.
Prior on-prem → cloud migration experience.
Bachelor’s/Master’s in Computer Science, Information Systems, or equivalent experience.

Nice To Haves

Highly Preferred OCP exp
Google Cloud Professional Cloud Architect/DevOps/OCP (required or within 3 months).
Professional Data Engineer, Security Engineer.

Responsibilities

Designing data platforms on Google Cloud Platform (GCP).
Designing and implementing data lake architectures (e.g., Bronze/Silver/Gold or layered models).
Designing GCS buckets, including layout, naming conventions, lifecycle policies, and access controls.
Building batch and streaming ingestion pipelines using GCP-native services.
Designing robust error handling, replay, and backfill mechanisms for data ingestion.
Developing scalable batch and streaming pipelines using Dataflow (Apache Beam) and/or Spark (Dataproc).
Implementing data catalogs, metadata management, and ownership models.
Defining and enforcing data quality frameworks, including validation, freshness checks, and alerting.
Implementing fine-grained access controls for BigQuery and GCS.
Assisting with Sprint planning and providing technical guidance to the team.
Communicating effectively with stakeholders and providing solution architecture expertise.