Tide · job-boards.greenhouse.io · checked today
Lead Engineer, Data Platform
Skills, with evidence
- Airflow / orchestration
Our data platform runs on GCP: BigQuery as our warehouse, Fivetran and event-driven ingestion, dbt, Apache Airflow (AWS MWAA), Looker, Atlan, Databricks, Terraform, GitHub, ConductorOne and Okta, Coralogix, and Claude for AI-assisted development.
must have - Cost & performance
Find the main cost drivers in data platforms and pipelines, and add guardrails and attribution that teams can act on.
must have - Data modelling
Solid data engineering fundamentals: batch and streaming ingestion, CDC and event-driven patterns, data modelling, and correctness (idempotency, ordering, late data, backfill).
must have - Data quality
Set SLOs, freshness and quality checks, and alerting that tells on-call what matters.
must have - Idempotency & backfills
Solid data engineering fundamentals: batch and streaming ingestion, CDC and event-driven patterns, data modelling, and correctness (idempotency, ordering, late data, backfill).
must have - Python
Strong production engineering in Python and SQL, including building and operating APIs, pipelines or shared libraries.
must have - SQL
Strong production engineering in Python and SQL, including building and operating APIs, pipelines or shared libraries.
must have - dbt
Our data platform runs on GCP: BigQuery as our warehouse, Fivetran and event-driven ingestion, dbt, Apache Airflow (AWS MWAA), Looker, Atlan, Databricks, Terraform, GitHub, ConductorOne and Okta, Coralogix, and Claude for AI-assisted development.
must have - Governance & security
Working knowledge of data governance in practice: PII masking, access control, residency, retention and lineage.
must have · not practised here - Terraform
Practical infrastructure as code (Terraform or equivalent) and CI/CD for data and infrastructure, including IAM across environments.
must have · not practised here - Failure handling
Set SLOs, freshness and quality checks, and alerting that tells on-call what matters.
- Streaming
Solid data engineering fundamentals: batch and streaming ingestion, CDC and event-driven patterns, data modelling, and correctness (idempotency, ordering, late data, backfill).
- GCP
We own how data is produced, moved, stored, modelled, governed and served across the UK, EU and India, on GCP with BigQuery at the core.
not practised here - Incremental loads
Solid data engineering fundamentals: batch and streaming ingestion, CDC and event-driven patterns, data modelling, and correctness (idempotency, ordering, late data, backfill).
Your plan
- SQL
drills at the Staff level
- Python
drills at the Staff level
- Modelling
drills at the Staff level
- Pipelines
drills at the Staff level
- Spark
drills at the Staff level
The full plan, drill by drill, is on the job page.
leaves in 8 dremoved the moment Tide closes it