Associate - Analytics Data Engineer

Join a team focused on analytics enablement—designing analytics-ready data models and a trusted semantic layer that standardizes business metrics. You will partner with stakeholders to translate requirements into well-modeled datasets in Databricks and Snowflake, using SQL as your primary language and Python when needed. You will define consistent metrics and semantic conventions that enable scalable self-service reporting. You will help create a data ecosystem where consumers trust and reuse what has been built.

As an Analytics Solutions Associate within Analytics Engineering, you will build analytics-ready data models and a trusted semantic layer that standardizes business metrics. You will partner with stakeholders to translate requirements into governed, reusable datasets and semantic definitions. You will own the structure and usability of downstream analytics by defining grains, dimensions, facts, conformed entities, and metric logic so teams can move faster with confidence. You will collaborate with upstream engineering partners to ensure source-to-model alignment and ensure data quality and documentation meet a high bar. Success looks like consistent key performance indicator definitions across dashboards, clear semantic conventions, and performant, well-documented models.

• Lead development of analytics data models (dimensional and/or domain-oriented) optimized for reporting, business intelligence, and self-service consumption.
• Design and maintain a semantic layer (standardized metrics, dimensions, entities, and business definitions) to ensure consistency across dashboards and analyses.
• Translate stakeholder requirements into clear modeling deliverables (entities, grains, metric definitions, acceptance criteria).
• Build transformations primarily in SQL, leveraging Python when needed for complex logic, automation, or validation.
• Implement and champion data quality controls (tests, reconciliations, anomaly checks) tied to business-critical metrics.
• Optimize model performance in Snowflake and/or Databricks (efficient joins, partitioning and clustering strategies where applicable, cost/performance trade-offs).
• Collaborate with upstream teams on source system understanding (including semi-structured data and non-relational sources) and ensure analytics models reflect correct business meaning.
• Establish modeling standards: naming conventions, documentation, lineage, metric governance, and change management for semantic definitions.
• Support enablement by documenting curated datasets, creating user guidance, and helping consumers adopt the semantic layer correctly.

Minimum education and experience required: Master’s degree in Information Technology, Computer Science, Management Information Systems, Operations Research or related field of study plus 3 years of experience in the job offered or as Analytics Engineer or related occupation. The employer will alternatively accept a Bachelor’s degree in Information Technology, Computer Science, Management Information Systems, Operations Research or related field of study plus 5 years of experience in the job offered or as Analytics Engineer or related occupation.

• Advanced SQL skills (complex joins, performance tuning, incremental logic).
• Strong understanding of data modeling (facts and dimensions, grains, conformed dimensions, slowly changing dimensions, metric design).
• Demonstrated experience building or operating a semantic layer or metrics framework (tool-agnostic; ability to standardize key performance indicator logic and definitions).
• Comfort working with semi-structured data (JSON) and non-relational sources and modeling them for analytics.
• Exposure to data governance concepts (role-based access control, data classification, lineage, audit requirements).
• Working experience with Snowflake and/or Databricks in an analytics context.
• Practical Python skills for data workflows (validation, automation, notebooks and scripts).
• Ability to partner with stakeholders, clarify ambiguous requirements, and drive to measurable outcomes.
• Strong documentation habits and attention to data correctness.

• Experience with testing and documentation.
• Familiarity with business intelligence tooling and semantic consumption patterns (for example, Tableau, Sigma, or Looker concepts).
• Knowledge of orchestration and observability (for example, Airflow, Dagster, or Azure Data Factory; logging and alerting; service level agreement mindset).