Technical Lead

TESTQ Technologies LTD.; Sheffield; Ibrido; GBP 90.000 - 130.000; Tempo pieno

Descrizione del lavoro

TESTQ Technologies LTD. is seeking a full stack engineer to lead engineering delivery and technical direction for a scalable, multi-tenant data integration platform on Google Cloud Platform.

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. You will own the technical roadmap, drive CI/CD and observability, and mentor engineers while coordinating with

Competenze

• Proven experience leading data engineering platforms in cloud environments.
• Strong background in ETL/ELT pipelines and Airflow.
• Hands-on with Python and API design.
• Experience with GCP services and IaC (Terraform).
• Ability to translate complex topics for mixed audiences.

Mansioni

• Own the technical roadmap and align engineering outcomes to business priorities.
• Lead design for new integrations and platform capabilities.
• Set engineering standards and ensure adoption across the team.
• Contribute hands-on to critical ETLs and framework components.

Conoscenze

Technical leadership

ETL/ELT pipelines

Airflow

Google Cloud Platform

Python

CI/CD

Data lineage

Formazione

Bachelor’s degree (or equivalent)

Strumenti

Terraform

BigQuery

GKE

Descrizione del lavoro

We are looking for a full stack engineer who will lead the engineering delivery and technical direction for a scalable, multi-tenant data integration platform on Google Cloud Platform (GCP). The platform uses Managed Airflow (Composer) 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 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 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, architecture design, 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.