Analytics Engineer (AWS)

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.

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).