AWS Data Engineer with exp in 10+ years in analytics engineering, SQL, Python, semantic data model

Summary: 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

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).