Role
Google Cloud Data Architect – IAM Data Modernization
Location
Dallas, TX / Charlotte, NC / Iselin, NJ / Chandler, AZ / Ohio, Delaware (Hybrid)
Eligibility
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
- 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).
- Ability to design robust error handling, replay, and backfill mechanisms.
- 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.
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.