Data architect with Google Cloud Platform

Role: Data architect with Google Cloud Platform Exp

Location: Dallas, TX or Charlotte, NC (Onsite)

Duration: 12 Months

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 Google Cloud Platform 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 Google Cloud Platform-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 (Google Cloud Platform) (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

Google Cloud Platform certifications (preferred, not mandatory)