Job Summary
Role: Analytics Engineer (AWS)
You will own the semantic modelling and governed metrics layer on AWS,
building robust modular transformations and facilitate analytics . You will
ensure trust in metrics, performance at scale, and effective stakeholder
enablement.
Key Responsibilities
Key responsibilities
• Modelling and ELT: design star schemas and domain marts; implement tests
(schema, data, freshness), documentation, and incremental strategies.
• Metrics governance: Define and version business metrics (owners,
contracts, change control); implement semantic layer (dbt Semantic
Layer/MetricFlow) and ensure consistency across BI.
• BI delivery: Support building BI datasets and dashboards; implement
RLS/column level security; optimise SPICE, query performance, and UX.
• Quality and reliability: Add DQ checks in pipelines; monitor freshness
and accuracy; partner with Core Data Engineer on upstream contracts and SLAs.
• CI/CD and workflow: Git driven development, PR reviews, environment
promotion; automate model validation and BI artefact deployment.
• Performance tuning: Redshift sort/dist keys, WLM/concurrency scaling;
Athena partitioning and file formats for efficient queries.
• Enablement: Translate requirements, document definitions, run training,
and maintain a catalogue of metrics/datasets in Glue Catalog.
Outcomes (first 60–90 days)
• Ship a governed KPI suite (metric catalogue + dbt models) and at least
two business critical dashboards with RLS.
• Establish CI/CD for analytics repo with automated tests and promotions;
reduce dashboard query times via model and dataset tuning.
• Publish clear documentation for metrics, dimensions, lineage, and
ownership.
Skill Requirements
Skills and experience
• 10+ years in analytics engineering; expert SQL, solid Python; strong
semantic data modelling and documentation. Clear understanding of ABAC on Data
products.
• 3+ years in dbt (models/macros/tests/exposures) or Glue Data Governance,
Redshift/Serverless and/or Athena; Glue Catalog integration.
• AWS SMUS/Datazone experience strongly preferred.
• 3+ years delivering governed data products on cloud.
• 5+ years working with designing data architecture on medallion
architecture.
• Version control and CI/CD (GitHub); YAML/Jinja proficiency.
• Metric governance and change management; stakeholder engagement and
requirements translation.
Nice to have
• Experience with dbt Semantic Layer/MetricFlow, QuickSight Q,
Tableau/Power BI, Iceberg/Spectrum, Great Expectations.
• Familiarity with Lake Formation policies and policy as code approaches.
• Experience with data mesh/domain ownership and feature store patterns
(SageMaker Feature Store).