You are a strategic thinker passionate about driving solutions in Analytics . You have found the right team.
As a/an Associate Analytics Engineer within the Data & Analytics team, you will build analytics-ready data models and a trusted semantic layer that standardizes business metrics. You will translate stakeholder needs into governed datasets and KPI definitions in Snowflake and/or Databricks using SQL as the primary transformation language. You will embed data quality, documentation, and performance best practices so downstream teams can reliably reuse models for self-service reporting.
Job Responsibilities
- Lead development of dimensional and/or domain-oriented analytics data models optimized for BI and self-service consumption.
- Design and maintain a semantic layer defining standardized metrics, dimensions, entities, and business definitions.
- Translate stakeholder requirements into clear modeling deliverables, including grains, entities, metric logic, and acceptance criteria.
- Build transformations primarily in SQL and leverage Python for complex logic, automation, and validation as needed.
- Implement data quality controls, including tests, reconciliations, and anomaly checks tied to business-critical metrics.
- Optimize model performance in Snowflake and/or Databricks by applying efficient joins and storage/performance strategies.
- Collaborate with upstream teams to align source-to-model meaning, including semi-structured and NoSQL data considerations.
- Establish modeling standards covering naming conventions, documentation, lineage, metric governance, and change management.
- Document curated datasets and produce user guidance to enable correct adoption of semantic definitions.
- Support consumers by troubleshooting metric questions and improving usability of downstream analytics products.
Required Qualifications, Capabilities, and Skills
- Hold a Master’s degree in IT, Computer Science, MIS, Operations Research, or related field plus 3 years of relevant experience, or hold a Bachelor’s degree in the same fields plus 5 years of relevant experience.
- Demonstrate advanced SQL capability, including complex joins, performance tuning, and incremental logic.
- Apply strong data modeling expertise across grains, facts/dimensions, conformed dimensions, SCDs, and metric design.
- Build or operate a semantic layer or metrics framework to standardize KPI logic and definitions.
- Model semi-structured data (e.g., JSON) and integrate NoSQL sources for analytics use cases.
- Use Snowflake and/or Databricks effectively in an analytics engineering context and apply practical Python for workflow automation and validation.
- Practice strong stakeholder partnership and documentation discipline to drive clarity, correctness, and measurable outcomes.
Preferred Qualifications, Capabilities, and Skills
- Leverage experience with testing and documentation frameworks for analytics engineering.
- Apply familiarity with BI consumption patterns and tooling concepts (e.g., Tableau, Sigma, Looker).
- Utilize orchestration tooling knowledge (e.g., Airflow, Dagster, ADF) with an SLA and reliability mindset.
- Implement observability practices such as logging, alerting, and operational runbooks for data products.
- Optimize cost and performance trade-offs through pragmatic platform tuning and design decisions.