GCP data Engineer

In this role, you will be responsible for transforming raw operational and master data sources into clean, lineage-tracked, governed, and queryable knowledge assets. You will design and support data connectivity, metadata management, graph platforms, ingestion frameworks, and search capabilities that enable scalable knowledge discovery and enterprise intelligence solutions.

The ideal candidate will possess strong expertise in Google Cloud Platform (GCP), data engineering, metadata management, graph technologies, data lineage, and scalable ingestion architectures.

Work Model

This position is fully remote within the United States , providing flexibility while collaborating with globally distributed teams across multiple locations and time zones.

Occasional travel may be required for client workshops, planning sessions, architecture reviews, and strategic initiatives., Data Connectivity & Integration

• Design, develop, and maintain connectivity solutions for enterprise data sources.
• Build adapters and integration frameworks supporting:
• BigQuery
• Cloud SQL
• REST APIs
• Apigee
• Google Cloud Storage (GCS)
• Pub/Sub
• Establish scalable and reliable ingestion patterns for operational, transactional, and master data systems.
• Support API-based integrations across enterprise applications and data platforms.

Data Profiling & Metadata Management

• Profile enterprise data to assess:
• Data Quality
• Completeness
• Statistical Patterns
• Dependencies
• Data Relationships
• Source System Characteristics
• Develop data discovery frameworks that support ontology and semantic model development.
• Build metadata management processes supporting data governance, traceability, and analytics.
• Surface data quality issues and metadata findings to architecture and business teams.

Knowledge Graph & Search Platform Engineering

• Design and operate the enterprise Knowledge Core supporting graph-based data exploration and search.
• Implement graph and knowledge management platforms utilizing:
• Spanner Graph
• Neo4j
• Amazon Neptune
• TigerGraph
• RDF/SPARQL Technologies
• Equivalent Graph Platforms
• Support graph data modeling, relationship management, and semantic search capabilities.
• Enable enterprise-scale graph analytics and knowledge discovery use cases.

Batch & Streaming Data Engineering

• Build and maintain batch and real-time data pipelines that continuously synchronize source data with graph and catalog platforms.
• Design ingestion architectures utilizing:
• Dataflow
• Apache Beam
• Pub/Sub
• Cloud Composer
• Event-Driven Architectures
• Ensure data pipelines remain scalable, resilient, and cost-efficient.
• Support near real-time updates across ontology and knowledge graph environments.

Data Governance, Quality & Lineage

• Implement automated data quality controls and validation frameworks.
• Support schema discovery and metadata catalog capabilities.
• Capture and maintain end-to-end data lineage from source systems through graph and ontology representations.
• Establish governance controls including:
• IAM Policies
• Encryption Standards
• Metadata Tracking
• Lineage Instrumentation
• Partner with architecture and governance teams to ensure compliance with enterprise standards.

Ontology & AI Enablement

• Support ontology mapping activities by providing:
• Profiled Schemas
• Data Dependency Maps
• Sample Data Sets
• Metadata Context
• Deliver curated and well-profiled datasets supporting machine learning and entity-resolution initiatives.
• Collaborate with Data Scientists and Applied AI teams to improve data quality and knowledge graph accuracy.
• Enable graph-based AI use cases, entity matching, semantic search, and inference capabilities.

Monitoring, Reliability & Optimization

• Monitor platform health, ingestion jobs, graph-processing workloads, and metadata services.
• Troubleshoot data and pipeline issues and implement preventative solutions.
• Optimize performance, scalability, and operational efficiency across the platform stack.
• Manage cost optimization initiatives across cloud infrastructure and processing workloads., * Google Cloud Platform (GCP)
• BigQuery
• Cloud SQL
• Dataflow
• Pub/Sub
• Cloud Composer
• Dataplex
• Spanner Graph
• Neo4j
• Amazon Neptune
• TigerGraph
• RDF/SPARQL
• Metadata Management
• Data Lineage
• Data Quality
• Knowledge Graphs
• Semantic Search
• Vector Search
• Python
• SQL
• API Integration
• Data Governance

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Science, or a related discipline, or equivalent work experience.
• 5+ years of experience in Data Engineering or Data Platform Engineering.
• Proven experience building large-scale ingestion, metadata, and data management solutions.
• Strong expertise in:
• Python
• SQL
• Hands-on experience with Google Cloud Platform (GCP), including:
• BigQuery
• Dataflow
• Pub/Sub
• Cloud Composer
• Cloud SQL
• Google Cloud Storage (GCS)
• Experience implementing:
• Data Profiling
• Data Lineage
• Metadata Management
• Data Quality Frameworks
• Experience with graph technologies, including one or more of:
• Spanner Graph
• Neo4j
• Amazon Neptune
• TigerGraph
• JanusGraph
• RDF/SPARQL Platforms
• Experience writing graph queries using:
• Cypher
• Gremlin
• SPARQL
• Equivalent Graph Query Languages
• Strong understanding of data governance and metadata management principles.
• Excellent troubleshooting, analytical, and problem-solving skills.

Preferred Qualifications

• Hands-on experience with Spanner Graph.
• Experience within telecommunications environments.
• Knowledge of:
• OSS/BSS Systems
• TM Forum SID
• eTOM Frameworks
• Experience with Apigee or enterprise API management platforms.
• Google Cloud Professional Data Engineer Certification.
• Experience building semantic search, knowledge graph, or ontology-based solutions.
• Familiarity with vector search technologies and retrieval architectures.
• Experience supporting AI, knowledge management, or graph-powered analytics initiatives.

Technical Expertise

The annual salary for this position is anticipated to be between $100,000 and $120,000 USD , depending on experience, qualifications, geographic location, and other factors permitted by law.

In addition to base salary, this position may be eligible for:

• Annual performance-based bonus
• Merit-based compensation increases
• Employee referral bonuses, where applicable

The compensation information provided is accurate as of the date of this posting. Cognizant reserves the right to modify compensation information based on business needs and applicable laws., Cognizant offers a comprehensive benefits package, including:

• Medical, Dental, and Vision Insurance
• Health Savings Account (HSA) and Flexible Spending Accounts (FSA)
• Life Insurance and AD&D Coverage
• Short-Term and Long-Term Disability Insurance
• Paid Time Off (PTO) and Company Holidays
• 401(k) Plan with Company Contributions (subject to eligibility)
• Employee Stock Purchase Plan (where available)
• Employee Assistance Program (EAP)
• Parental Leave and Family Care Benefits
• Tuition Reimbursement and Professional Development Programs
• Access to Learning, Certification, and Career Development Opportunities

Benefits are subject to eligibility requirements and plan terms.

Why Cognizant

• Build next-generation knowledge graph and ontology platforms.
• Work with cutting-edge graph, metadata, cloud, and AI technologies.
• Collaborate with architects, data scientists, and platform engineering teams on enterprise-scale initiatives.
• Gain exposure to advanced semantic search, vector search, and AI-powered knowledge management solutions.
• Access industry-leading learning, cloud certifications, and career development programs.
• Join a global organization committed to innovation, engineering excellence, and digital transformation.