Role: Google Cloud Data Architect – IAM Data Modernization

Location: Dallas, TX / Charlotte, NC/ Iselin, NJ, / Chandler, AZ / Ohio, Delaware (Hybrid)

*Must be a US Citizen/ GC only

About Position:

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.

What You'll Do:

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

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

Good to have

Security, Privacy & Compliance

· 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

Expertise You'll Bring:

Experience: [10–14]+ years in DevOps and Data Architecture, 5+ years designing on Pyspark/GCP/OCP 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/DevOps/OCP (required or within 3 months). Plus: Professional Data Engineer, Security Engineer

Flexible work from home options available.