Data Engineer at LinkedIn

London, Hertfordshire - England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

13 Jan, 27

Salary

45000.0

Posted On

15 Oct, 26

Experience

1 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Industry

Information Technology & Services;Software Development & Publishing

Description

About the job

Job Title

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

Role purpose

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.

Data lineage & observability

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.

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Responsibilities