Lead Data Architect

Webologix Ltd/ INC; Sheffield; On-site; GBP 90,000 - 120,000; Full time

Job summary

Webologix Ltd/ INC is seeking a Technical Lead to drive a multi-tenant data integration platform on Google Cloud. You’ll own the technical roadmap, design scalable ETL pipelines in Apache Airflow, and lead a team through architecture, testing, and deployment improvements.

The role combines hands-on coding with strategic leadership, focusing on reliability, data lineage, and end-to-end observability across dev, test and prod environments. Sheffield-based, on-site work with modern cloud tech stack.

Qualifications

• Proven experience as a Technical Lead/Lead Engineer delivering data engineering or integration platforms in cloud environments.
• Strong hands-on experience building production-grade ETL/ELT pipelines and orchestration with Apache Airflow.
• Solid experience on Google Cloud Platform services (BigQuery, Cloud Storage, Pub/Sub, Dataflow) and/or container platforms (GKE, Cloud Run).
• IAM, networking, secrets management, and environment separation.
• Strong software engineering foundations: Python, APIs, configuration management, testing strategy, and performance tuning.
• CI/CD expertise (pipeline tooling, release controls, automated quality gates) and Infrastructure-as-Code (Terraform).
• Experience designing for reliability and observability, including retries, idempotency, and incident resilience.
• Ability to translate complex technical topics for mixed audiences and drive alignment across teams.

Responsibilities

• Own the technical roadmap for the platform and align outcomes with business priorities.
• Lead solution design for new integrations and platform capabilities with maintainability in mind.
• Set engineering standards and ensure adoption across the team.
• Provide hands-on contribution when needed (critical ETLs, framework components, design spikes).
• Design and develop robust ETL pipelines orchestrated in Apache Airflow with reliable scheduling and tracing.
• Establish patterns for onboarding new source systems and delivering data to downstream vendors with traceability.
• Drive data quality controls and operational runbooks.
• Evolve multi-tenant architecture and optimise for performance, cost and reliability across dev/test/prod.
• Improve build and deployment automation for platform services and Airflow DAGs; implement CI/CD best practices.
• Enhance observability with logging, metrics, alerts and dashboards to improve MTTR and change failure rate.
• Mentor engineers through code reviews and structured coaching; foster continuous improvement.

Skills

Technical leadership

ETL/ELT pipelines

Apache Airflow

Google Cloud Platform

Python

APIs

CI/CD

Terraform

Data lineage

Observability

Tools

Airflow

BigQuery

Cloud Storage

Pub/Sub

Dataflow

GKE

Cloud Run

Terraform

Job description

Technical Lead - Multi-tenant Data Integration Platform (GCP / Airflow)

Lead the engineering delivery and technical direction for a scalable, multi-tenant data integration platform on Google Cloud Platform (GCP). The platform uses Apache Airflow to orchestrate ETL pipelines and enables reliable movement of data from source systems through the platform into downstream vendor systems. You’ll guide the team in building high-quality ETLs, improving automation and deployment pipelines, and implementing end-to-end data lineage with clear, visual traceability of data movement.

Key responsibilities

Technical leadership & delivery

• Own the technical roadmap for the platform, aligning engineering outcomes to business priorities and operational resilience.
• Lead solution design for new integrations and platform capabilities, balancing pace with long-term maintainability.
• Set engineering standards (coding, testing, documentation, observability, security) and ensure consistent adoption across the team.
• Provide hands-on contribution where needed (critical ETLs, framework components, design spikes, performance fixes).

ETL orchestration & data movement

• Design and develop robust ETL pipelines orchestrated in Apache Airflow, including scheduling strategies, dependency management, retries, backfills, and processing.
• Establish patterns for onboarding new source systems and delivering data to downstream vendor systems with strong reliability and traceability.
• Drive data quality controls (validation, reconciliation, exception handling) and operational runbooks.

Platform engineering (multi-tenancy & scalability)

• Evolve the multi-tenant architecture: tenant isolation, configuration management, metadata-driven pipelines, and scalable runtime patterns.
• Optimise for performance, cost, and reliability across environments (dev/test/prod), including capacity planning and operational SLAs.

Automation, CI/CD and release engineering

• Improve build and deployment automation for both platform services and Airflow DAGs (versioning, packaging, promotions, approvals).
• Implement CI/CD best practices: automated testing, static analysis, security scanning, and controlled rollout/rollback strategies.
• Reduce manual toil by investing in self-service onboarding, templates, and engineering enablement tooling.
• Implement end-to-end data lineage (source platform vendor), including metadata capture and a visual representation that supports auditability and faster incident resolution.
• Enhance observability across pipelines and services: logging, metrics, alerting, and dashboards; drive measurable improvements in MTTR and change failure rate.

Stakeholder management & ways of working

• Partner with product owners, data owners, architects, vendors, and governance teams to shape requirements and manage delivery expectations.
• Lead agile ceremonies and engineering planning; remove blockers and maintain delivery momentum.
• Mentor engineers through code reviews, design reviews, and structured coaching; foster a culture of continuous improvement.

Required experience & skills

• Proven experience as a Technical Lead / Lead Engineer delivering data engineering or integration platforms in cloud environments.
• Strong hands-on experience building production-grade ETL/ELT pipelines and orchestration with Apache Airflow.
• Solid experience on Google Cloud Platform, such as:
• Data services (e.g., BigQuery, Cloud Storage, Pub/Sub, Dataflow) and/or container platforms (e.g., GKE, Cloud Run)
• IAM, networking, secrets management, and environment separation
• Strong software engineering foundations: Python, APIs, configuration management, testing strategy, and performance tuning.
• CI/CD expertise (pipeline tooling, release controls, automated quality gates) and Infrastructure-as-Code experience (e.g., Terraform).
• Experience designing for reliability: retries, idempotency, dead-letter patterns, backpressure handling, and operational resilience.
• Ability to translate complex technical topics for mixed audiences and drive alignment across teams.

Desirable (nice to have)

• Experience implementing or integrating data lineage/metadata management tooling (e.g., Data Catalog-style metadata, OpenLineage, DataHub, etc.).
• Experience with multi-tenant platforms (tenant isolation models, metadata-driven frameworks).
• Knowledge of data governance concepts (data quality, auditability, retention, access controls).
• Experience integrating with external vendor systems and managing vendor technical dependencies.

Success measures (what “good” looks like)

• Faster, safer delivery of new integrations via improved automation and CI/CD.
• Measurable improvement in pipeline reliability and operational outcomes (reduced incidents, faster recovery).
• Clear, trusted end-to-end lineage view for key datasets and integrations.
• Consistent engineering standards and a team that’s growing capability and confidence over time.

Working style

A pragmatic, hands-on technical leader who enjoys getting things done, raises engineering standards without slowing delivery, and builds an inclusive team environment where different viewpoints improve outcomes.