Analytics Engineer Jobs

About The Position

Requirements

• 4+ years building governed analytics models and BI assets in a modern data warehouse.
• Strong SQL on Snowflake, including window functions, recursive CTEs, and incremental MERGE patterns.
• Production dbt experience: models, tests, snapshots, documentation, and CI runs.
• Tableau (required) and Power BI (strongly preferred) at production quality, including parameterized dashboards, row-level security, and performance tuning on Snowflake-backed extracts or live connections.
• Git and GitHub workflows with code review discipline.
• Daily use of Claude Code, Cursor, or equivalent agent tooling with concrete examples of faster shipping.
• Strong written communication skills, able to explain metric definitions to finance leaders and SQL patterns to engineers.

Nice To Haves

• dbt Semantic Layer.
• Snowflake Cortex Analyst or Cortex Search in production.
• GitHub Actions CI/CD for dbt projects.
• Python for data work (pandas, snowflake-snowpark-python, ad-hoc scripting).
• Domain experience in travel, hospitality, cruise, or consumer finance.
• Streamlit in Snowflake.
• Salesforce Data Cloud or Salesforce Marketing Cloud reporting.
• Power Automate flows for alert and notification routing.
• Familiarity with Seaware, Oracle, or other reservation system data models.
• Finance domain depth: revenue recognition, occupancy denominators, AOP targets.

Responsibilities

• Design governed data marts.
• Build the semantic layer that powers scorecards.
• Partner directly with Finance, Marketing, Revenue Management, Operations, and Reservations to turn ambiguous business questions into trusted models and dashboards.
• Apply software engineering practices to analytics: version control, dbt tests, CI/CD, and clear documentation.
• Build dbt models in our medallion layout using staging, intermediate, and mart models with explicit grain. Use SCD2 snapshots where business questions span time.
• Write performant SQL in Snowflake, read query profiles when something is slow, and use clustering and warehouse sizing deliberately.
• Apply consistent naming conventions and audit columns across every mart.
• Define metrics in the dbt Semantic Layer with explicit dimensions, time grains, and ownership.
• Author Cortex Analyst semantic YAML for the marts that internal teams query through natural language.
• Maintain a single canonical definition for every business KPI, avoiding duplicate metric logic across Tableau, Power BI, and Streamlit.
• Build, optimize, and govern dashboards in Tableau Cloud and Power BI.
• Implement row-level and object-level security, usage monitoring, and deployment workflows.
• Audit and modernize legacy BI assets, retiring reports that are not used.
• Write dbt tests on every model: not-null, unique, relationships, accepted values, and custom business rules.
• Add freshness checks and Snowflake Alerts to gold and reporting models.
• Track SLAs for the marts that feed leadership-facing reporting.
• Translate ambiguous requests from Finance, Marketing, Revenue Management, Operations, and Reservations into models the rest of the team can also build on.
• Write documentation for future reference.
• Partner with the Senior Data Engineer to ensure source pipelines are designed correctly upstream.
• Use Claude Code as the primary working environment, including the shared data-team-skills plugin library.
• Use Snowflake Cortex (Complete, Search, Analyst) to build natural-language interfaces, summarize text columns, classify free-text, and accelerate exploratory analysis.
• Use multi-model review through zen-mcp when designing new metric definitions or auditing complex SQL refactors.