TEKsystems
GCP Architect
London
Posted about 18 hours ago
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The Practice Architect
The Practice Architect is a client-facing architecture and delivery leadership role within TEKsystems Global Services. The role establishes platform modernisation as the foundation for sustainable cloud adoption, with a specialist focus on modern data services on Google Cloud Platform (GCP).
The architect will shape and assure transformation programmes that move applications, databases and data platforms from on-premises environments, AWS or Microsoft Azure to GCP. They will translate business outcomes into target architectures, migration roadmaps and executable delivery plans, while balancing security, resilience, operability, performance, cost and organisational readiness.
The role requires deep capability across the key GCP Data services such as BigQuery, PostgreSQL and AlloyDB, together with experience designing unified data platforms, bringing together analytical, operational, streaming and lakehouse data patterns.
Experience with Gemini and AI-enabled data solutions is highly advantageous, but not considered mandatory.
Responsibilities
Platform Modernisation Architecture
- Assess existing application, infrastructure, database and data-platform estates; identify technical debt, constraints, dependencies and modernisation opportunities.
- Define current-state, transition-state and target-state architectures aligned to business priorities and measurable transformation outcomes.
- Select appropriate modernisation paths, including rehost, replatform, refactor, repurchase, retain and retire, and document the rationale and trade-offs.
- Design GCP landing-zone and platform capabilities covering organisation and project structure, identity, networking, security controls, observability, backup, disaster recovery, policy, automation and cost management.
- Promote cloud-native engineering, infrastructure as code, CI/CD, SRE, platform engineering and product-oriented operating models.
- Ensure non-functional requirements are translated into practical architecture decisions and acceptance criteria.
GCP Data Services & Unified Data Platforms
- Own solution architecture for BigQuery-based analytical platforms, including data modelling, workload design, ingestion, transformation, performance, security, governance and cost optimisation.
- Design PostgreSQL modernisation patterns using Cloud SQL for PostgreSQL and AlloyDB, including compatibility assessment, schema and code remediation, high availability, read scaling, backup, recovery and operational integration.
- Define unified data-platform architectures spanning data warehouse, lakehouse, operational databases, streaming, data integration, metadata, governance and data-product consumption.
- Apply appropriate GCP services and patterns, which may include Cloud Storage, BigLake, Dataplex, Dataflow, Pub/Sub, Datastream, Data Fusion, Dataform, Composer, Dataproc, Bigtable, Spanner and Looker.
- Create data-domain, canonical modelling, interoperability and data-quality approaches that enable trusted reuse across applications, analytics and AI.
- Guide teams on privacy, encryption, access control, lineage, retention and regulated-data considerations in partnership with security and governance stakeholders.
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Migration & Transformation Delivery
- Lead migrations from on-premises data centres to GCP, including database, warehouse, lake, integration and application dependencies.
- Lead cross-cloud migrations from AWS to GCP, including source patterns such as Amazon S3, Redshift, RDS / Aurora PostgreSQL, Glue, EMR, Kinesis and associated data pipelines.
- Lead cross-cloud migrations from Microsoft Azure to GCP, including source patterns such as Azure Data Lake Storage, Synapse Analytics, Azure SQL / PostgreSQL, Data Factory, Databricks and Event Hubs.
- Define migration factories and repeatable delivery patterns covering discovery, sizing, proof of concept, remediation, data movement, validation, reconciliation, cutover, rollback and hypercare.
- Choose between offline, online, replication, federation, coexistence and phased-modernisation approaches based on service levels, data volume, complexity and business risk.
- Own architecture decisions and risks, support change control, and provide clear escalation and resolution paths across programme stakeholders.
Client Leadership, Consulting & Pre-Sales
- Lead client workshops, architecture discovery and executive-level conversations, converting ambiguous requirements into a clear transformation narrative and roadmap.
- Contribute to proposals, statements of work, estimates, assumptions, dependencies, delivery models and technical presentations.
- Explain architecture options and trade-offs to technical and non-technical stakeholders, using evidence and outcome-focused recommendations.
- Partner with delivery leaders, engineers, data specialists, security teams and client stakeholders to maintain solution integrity throughout delivery.
- Mentor architects and engineers, review designs and promote consistent engineering quality across engagements.
Practice Development
- Develop and maintain reference architectures, assessment frameworks, migration playbooks, reusable patterns and estimation assets.
- Capture intellectual property and lessons learned from engagements and convert them into improvements to offerings and delivery methods.
- Support capability building through mentoring, technical communities, hiring support and knowledge sharing.
- Maintain awareness of GCP product evolution and evaluate where new capabilities can create relevant client value.
Qualifications
- Bachelor’s/master’s degree in computer science, Engineering, Data Science, Artificial Intelligence, Mathematics, or a related technical field, or equivalent, relevant experience.
- A minimum of 5-8+ years of overall experience in Information Technology, with a demonstrable focus on AI/ML development, data science, or data engineering.
- At least 2-4+ years of hands-on experience in developing, training, and deploying machine learning models, with increasing exposure to and utilization of Google Cloud Platform AI services.
- Experience working as an effective member of a team on AI/ML projects, contributing to various phases of the project lifecycle from data preparation to model deployment.
- Familiarity with the core concepts of cloud computing and specific GCP services related to data storage, data processing, and machine learning (e.g., Google Cloud Storage, BigQuery, Vertex AI basics).
- Experience with data preprocessing, feature engineering, and working with large datasets.
- Exposure to deploying models using containerization (e.g., Docker) and familiarity with basic MLOps concepts.
- Developing client interaction skills, with some experience in explaining technical concepts or project updates to stakeholders.
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Technical Skills
Mandatory
- GCP organisational design, landing zones, IAM, networking, security, observability, resilience, FinOps, infrastructure as code, CI/CD and platform engineering.
- BigQuery, Cloud Storage, BigLake, Dataplex, Dataform, Dataflow, Pub/Sub, Datastream and appropriate orchestration and integration services.
- PostgreSQL, Cloud SQL for PostgreSQL, AlloyDB, database assessment, compatibility, migration, performance, high availability, disaster recovery and operational support patterns.
- Discovery and assessment, dependency mapping, migration waves, data transfer and replication, validation, reconciliation, cutover, rollback and decommissioning.
- Architecture knowledge sufficient to map AWS and Azure services, constraints and operating models to appropriate GCP target patterns.
- Reference architecture, architecture decision records, non-functional requirements, risk management, governance, estimation and delivery assurance.
Highly Advantageous but not Mandatory
- Experience with Gemini for Google Cloud, Gemini in BigQuery, Vertex AI or generative AI solutions that use enterprise data.
- Experience designing data foundations for retrieval-augmented generation, semantic search, vector workloads, agents or AI-assisted analytics.
- Knowledge of BigQuery ML, AlloyDB AI capabilities, model governance, MLOps or responsible AI controls.
- Experience with open table formats, lakehouse architectures, data mesh, domain-oriented data products or cross-cloud data interoperability.
Soft Skills
- Good oral and written communication skills (English language), with an ability to articulate technical details clearly to both technical and non technical personnel.
- Strong conceptual and analytical skills, demonstrating an aptitude for problem-solving and learning new technologies.
- A team player with experience collaborating effectively within technical teams to deliver successful solutions.
- Proven ability to appropriately prioritize and plan work in a rapidly changing environment.
- Eagerness to learn and develop architectural skills, including solution design, client communication, and technical leadership.
- Good organizational skills and attention to detail.
Certifications
- Google Cloud Professional Cloud Architect certification – Preferred.
- Google Cloud Professional Data Engineer certification – Preferred.
- Relevant database, security, networking, DevOps, AWS or Azure certifications – Beneficial.
- Equivalent demonstrable delivery experience may be considered alongside formal certification.
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