{"id":2447808,"url":"https://alion.io/job/test-triangle-technical-lead-lead-engineer","title":"Technical Lead / Lead Engineer","company":{"id":4380438,"name":"Test Triangle","domain":"testtriangle.com","url":"https://alion.io/company/testtriangle","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Zoho Recruit","truth_index":null},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":77000,"max_usd":130000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":85},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Google GKE","optional":false},{"name":"IAM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-13T13:59:26Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T11:09:27Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Title\nTechnical Lead\nRole purpose\nWe 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.\nKey responsibilities\nTechnical leadership & delivery\nOwn the technical roadmap for the platform, aligning engineering outcomes to business priorities and operational resilience.\nLead solution design for new integrations and platform capabilities, balancing pace with long-term maintainability.\nSet engineering standards (coding, testing, documentation, observability, security) and ensure consistent adoption across the team.\nProvide hands-on contribution where needed (critical ETLs, framework components, design spikes, performance fixes).\nETL orchestration & data movement\nDesign and develop robust ETL pipelines orchestrated in Airflow, including scheduling strategies, dependency management, retries, backfills, and processing.\nEstablish patterns for onboarding new source systems and delivering data to downstream vendor systems with strong reliability and traceability.\nDrive data quality controls (validation, reconciliation, exception handling) and operational runbooks.\nPlatform engineering (multi-tenancy & scalability)\nEvolve the multi-tenant architecture: tenant isolation, configuration management, metadata-driven pipelines, and scalable runtime patterns.\nOptimise for performance, cost, and reliability across environments (dev/test/prod), including capacity planning and operational SLAs.\nAutomation, CI/CD and release engineering\nImprove build and deployment automation for both platform services and Airflow DAGs (versioning, packaging, promotions, approvals).\nImplement CI/CD best practices: automated testing, static analysis, security scanning, and controlled rollout/rollback strategies.\nReduce manual toil by investing in self-service onboarding, templates, and engineering enablement tooling.\nData lineage & observability\nImplement end-to-end data lineage (source → platform → vendor), including metadata capture and a visual representation that supports auditability and faster incident resolution.\nEnhance observability across pipelines and services: logging, metrics, alerting, and dashboards; drive measurable improvements in MTTR and change failure rate.\nStakeholder management & ways of working\nPartner with product owners, data owners, architects, vendors, and governance teams to shape requirements and manage delivery expectations.\nLead agile ceremonies and engineering planning; remove blockers and maintain delivery momentum.\nMentor engineers through code reviews, design reviews, and structured coaching; foster a culture of continuous improvement.\nRequired experience & skills\nProven experience as a Technical Lead / Lead Engineer delivering data engineering or integration platforms in cloud environments.\nStrong hands-on experience building production-grade ETL/ELT pipelines and orchestration withAirflow.\nSolid 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)\nIAM, networking, secrets management, and environment separation\n\nStrong software engineering foundations: Python, APIs, configuration management, architecture design, testing strategy, and performance tuning.\nCI/CD expertise (pipeline tooling, release controls, automated quality gates) and Infrastructure-as-Code experience (e.g., Terraform).\nExperience designing for reliability: retries, idempotency, dead-letter patterns, backpressure handling, and operational resilience.\nAbility to translate complex technical topics for mixed audiences and drive alignment across teams.\nDesirable (nice to have)\nExperience implementing or integrating data lineage /metadata management tooling (e.g., Data Catalog-style metadata, OpenLineage, DataHub, etc.).\nExperience with multi-tenant platforms (tenant isolation models, metadata-driven frameworks).\nKnowledge of data governance concepts (data quality, auditability, retention, access controls).\nExperience integrating with external vendor systems and managing vendor technical dependencies.\nSuccess measures (what “good” looks like)\nFaster, safer delivery of new integrations via improved automation and CI/CD.\nMeasurable improvement in pipeline reliability and operational outcomes (reduced incidents, faster recovery).\nClear, trusted end-to-end lineage view for key datasets and integrations.\nConsistent engineering standards and a team that’s growing capability and confidence over time.\nWorking style\nA 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 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