Job Summary
Role: Analytics Engineer (AWS)
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).
Key Responsibilities
2. Writing complex sql queries for data extraction, transformation, and loading
3. Developing python scripts to automate data processing tasks
4. Creating and maintaining data pipelines for efficient data flow
5. Collaborating with cross functional teams to understand data requirements and deliver effective solutions
6. Troubleshooting and optimizing existing data processes for improved performance
7. Ensuring data quality and integrity throughout the data lifecycle
Skill Requirements
2. Strong sql skills for data querying and manipulation
3. Advanced knowledge of python programming for data processing and automation
4. Experience with data modeling, data warehousing, and data pipeline development
5. Familiarity with cloud platforms like aws for deploying data solutions
6. Analytical mindset with the ability to translate complex data requirements into technical solutions
7. Strong problem-solving skills and attention to detail
8. Excellent communication and teamwork abilities to collaborate effectively with stakeholders