Data Analytics Engineer

Job Description

Job Description

At AmaWaterways, we believe meaningful careers begin with purpose, passion and a shared commitment to delivering unforgettable experiences. For those who value curiosity, connection and personal enrichment, AmaWaterways offers the opportunity to help craft meaningful river journeys that invite travelers to follow their own current. Built on a foundation of heartfelt hospitality, we treat our guests—and each other—with genuine care, warmth and respect. AmaWaterways fosters a collaborative environment both onboard our ships and across our global network of offices, where team members grow together, support one another and take pride in upholding the high standards and thoughtful service our company is known for.

We invite talented, motivated professionals to explore our career opportunities and begin their journey with AmaWaterways today.

Role Summary

AmaWaterways is hiring a Data Analytics Engineer to own the analytics layer of our modern data platform. You will design governed data marts, build the semantic layer that powers our scorecards, and partner directly with Finance, Marketing, Revenue Management, Operations, and Reservations to turn ambiguous business questions into trusted models and dashboards. You will work on top of a Snowflake-native warehouse that is actively being built out, alongside a Senior Data Engineer who owns the ingestion plumbing. You will apply software engineering practices to analytics: version control, dbt tests, CI/CD, and clear documentation. You will also be an AI-native practitioner. Our daily environment is Claude Code, Snowflake Cortex, and dbt Cloud, and we expect you to use them fluently, not curiously.

What You Will Build

• Governed semantic models in dbt for our top business KPIs: bookings, occupancy, revenue, cancellations, retention, marketing performance, and operations metrics.
• Marts and reporting views on top of our medallion warehouse (Bronze, Silver, Gold, Reporting), with strict typing and clear grain documentation.
• Tableau and Power BI assets that share a single source of truth in the warehouse. No off-platform calculations.
• Cortex Analyst semantic YAML for natural-language data exploration by our internal users.
• Data quality tests on every model you author, with clear ownership of the freshness and accuracy SLAs.
• Companion views for the AMA Pulse scorecard (currently 72 KPIs across 894,000 rows of historical sailings).
• BI assets that consolidate analytical work currently spread across the AMA Pulse Streamlit app, Tableau Cloud, and ad-hoc SQL.

Day to Day Responsibilities

Modeling and SQL

• Build dbt models in our medallion layout. Use 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. Use clustering and warehouse sizing deliberately.
• Apply consistent naming conventions and audit columns across every mart.

Semantic layer and metric governance

• 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. No duplicate metric logic across Tableau, Power BI, and Streamlit.

BI development and governance

• 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. Retire reports that nobody opens.

Data quality and reliability

• Write dbt tests on every model: not-null, unique, relationships, accepted values, and custom business rules.
• Add freshness checks and Snowflake Alerts to your gold and reporting models.
• Track SLAs for the marts that feed leadership-facing reporting.

Stakeholder partnership

• Translate ambiguous requests from Finance, Marketing, Revenue Management, Operations, and Reservations into models the rest of the team can also build on.
• Write the kind of documentation your future self will want to read.
• Partner with the Senior Data Engineer to make sure the source pipelines you depend on are designed correctly upstream.

AI-native analytics engineering

• Use Claude Code as your primary working environment, including our 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 you are designing a new metric definition or auditing a complex SQL refactor.

Required Qualifications

• 4+ years building governed analytics models and BI assets in a modern data warehouse.
• Strong SQL on Snowflake. You can write window functions, recursive CTEs, and incremental MERGE patterns without searching the docs.
• 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.
• You already use Claude Code, Cursor, or equivalent agent tooling daily, with concrete examples of what you ship faster because of it.
• Strong written communication. You can explain a metric definition to a Finance leader and a SQL pattern to an engineer in the same week.

Strongly Preferred

• 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.

Nice to Have

• 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.