Data Architect OCP (OpenShift) | IAM Data Modernization

Indent :PSL216927_1-26-1
Role : Data Architect – OCP (OpenShift) | IAM Data Modernization
Location : Dallas, TX / Charlotte, NC (Hybrid – 3 days office)
Rate: $85/hr to $90/hr
Project/Program
Identity & Access Management (IAM) Data Modernization
Migration of an on-premises SQL data warehouse to a modern enterprise Data Lake platform, enabling analytics and GenAI use cases. The platform leverages PySpark-based processing, CI/CD pipelines, and containerized deployments on OpenShift (OCP), with GCP as a preferred cloud platform, to deliver scalable, secure, and high-performance data solutions
About Program/Project
The IAM Data Modernization program focuses on transforming legacy data platforms into a scalable and cloud-compatible architecture.
Key Highlights:

• Integration Scope: 30+ source systems with multiple downstream integrations [
• Capabilities: Metrics, reporting, advanced analytics, and GenAI use cases (NL querying, summarisation, cross-domain insights)
• Benefits:
  • Scalable and resilient data platform
  • High-performance semantic and analytics layer
  • Single source of truth for enterprise-wide reporting and analytics

Role Summary
We are looking for a Data Architect with strong expertise in OpenShift (OCP), PySpark, and CI/CD pipelines to design and govern scalable data platforms.
The role requires defining end-to-end data architecture, containerised deployment patterns, orchestration strategies (Airflow/Autosys), and platform standards, along with hands-on involvement in implementation.
Key Responsibilities
Data Architecture & Platform Design

• Define enterprise data architecture for IAM data lake and analytics platform
• Design scalable, modular, and containerised data pipeline architectures on OCP
• Establish data models, schema governance, and data lifecycle strategies
• Define best practices for data partitioning, performance optimisation, and cost efficiency

OpenShift (OCP) & Platform Engineering

• Architect and govern containerised data workloads on OpenShift (OCP)
• Define standards for deployment, scaling, and workload isolation
• Collaborate with DevOps teams for platform engineering and infrastructure alignment

Big Data & Processing (PySpark Focus)

• Define architecture for PySpark-based batch and near real-time processing pipelines
• Provide guidance on distributed processing design, optimisation, and performance tuning
• Establish reusable frameworks for ETL/ELT processing

Data Ingestion & Orchestration

• Architect data ingestion frameworks (batch, streaming, CDC)
• Define orchestration strategies using Airflow / Autosys
• Implement standards for retry, backfills, dependency management, and error handling

DevOps / CI-CD

• Define and oversee CI/CD strategy for data and platform deployments
• Enable automation of build, test, and deployment processes
• Ensure integration of CI/CD pipelines with OCP-based environments

Cloud & Data Platforms (Preferred)

• Provide architecture guidance for GCP-based data platforms (preferred, not mandatory)
• Define integration patterns for cloud-native and on-premise hybrid environments
• Guide teams on cloud migration strategies and modern data platform adoption

Data Governance, Quality & Observability

• Define frameworks for:
  • Data quality, validation, and lineage
  • Metadata management and cataloguing
• Establish monitoring, logging, alerting, and SLOs for platform reliability
• Ensure compliance with data security and audit requirements

Stakeholder Collaboration

• Work closely with client architects, IAM teams, and business stakeholders
• Translate business requirements into scalable technical architecture
• Provide architectural guidance and mentorship to engineering teams

Required Skills
Core Skills (Must Have)

• Strong experience in:
  • OpenShift (OCP) / Kubernetes-based platforms
  • PySpark / Spark ecosystem
  • CI/CD implementation for data platforms
  • Airflow / Autosys orchestration tools
• Solid understanding of:
  • Data lake architectures (layered models)
  • ETL/ELT design patterns
  • Distributed data processing concepts

Data Engineering & Storage

• Expertise in:
  • Data formats: Parquet, ORC, Avro
  • Partitioning and performance tuning
  • Large-scale data modelling for analytics

Cloud (Preferred – Not Mandatory)

• Experience with Google Cloud Platform (GCP) (preferred)
• Exposure to services like BigQuery, Dataproc, Dataflow, GCS is a plus

Observability & Reliability

• Experience defining:
  • Monitoring, logging, alerting frameworks
  • Dashboards, SLOs, and operational runbooks

Good to Have

• Experience with IAM domain / cybersecurity data
• Understanding of data security and access control frameworks
• Exposure to GenAI-enabled data platforms
• Experience in Agile delivery and team leadership

Qualifications

• Experience:
  • 10–14+ years in Data Architecture / Data Engineering
  • Strong experience in OCP, PySpark, CI/CD, and orchestration frameworks
  • Prior experience in data modernisation / migration programs
• Education:
  Bachelor's/Master's in Computer Science, Information Systems, or equivalent
• Certifications (Preferred):
  • OpenShift / Kubernetes certifications
  • GCP certifications (preferred, not mandatory)

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