Analytics Engineer Jobs in Georgetown, TX (NOW HIRING)

This position is not eligible for visa sponsorship or sponsorship transfers.

The Analytics Engineer will be a core member of the team building a next-generation data and analytics platform and advancing a data warehouse first, AI-centric operating model.

Working in close partnership with the Head of Data & Analytics and Senior Data Engineer / Data Architect, this person will own the user experience and interaction layer of our data product—how users access, explore, understand, and act on data. Today that experience is primarily delivered through Tableau, but the role is intentionally platform-agnostic and will help shape how that experience evolves.

The Analytics Engineer will co-own the transformation of raw data into harmonized, business-ready data products, including data models, metrics, semantic layers, and governance. They will build the connection between our underlying data foundation and the operational, revenue, and strategic analytics our users depend on.

This is a product-building role, not a report-building role. We expect this person to deeply understand our users, anticipate their needs, and build reusable analytical capabilities ahead of individual requests—including new AI-enabled ways for users to interact with and derive value from our data.

Purpose

Own how users access, explore, understand, and act on data within a data warehouse first, AI-centric solution. Connect that user experience to trusted, reusable analytical data products by co-owning the transformation of raw data through the Harmonization and Reporting/Analytics layers, and build analytical solutions that move beyond traditional reporting to identify patterns, explain performance, anticipate outcomes, and improve operational, revenue, and strategic decision-making.

Essential Responsibilities

• Own the user interaction and experience layer of the data product, currently centered on Tableau, serving both internal business users and external customers. Develop a deep understanding of these distinct user communities and design scalable experiences that efficiently serve common needs while thoughtfully addressing where their needs differ.
• Act as a product and analytics thought partner to business stakeholders—listening deeply, challenging assumptions, and translating business problems into solutions that improve how users operate and make decisions. Balance stakeholder needs, technical realities, and long-term product direction rather than simply taking and fulfilling requirements.
• Co-own the Harmonization and Reporting/Analytics layers, transforming raw and staged data into trusted, reusable, business-ready analytical products. Design curated models, dimensions, facts, metrics, semantic layers, and transformation logic that create a durable connection between the data foundation and the user experience.
• Build analytical solutions that extend beyond traditional BI, applying appropriate analytical methods to identify patterns, explain performance, anticipate outcomes, and surface opportunities for action—across operational, revenue, and strategic use cases.
• Own where analytical logic belongs across the product, translating business concepts into governed data models, metrics, and experiences while moving reusable logic upstream from dashboards, spreadsheets, extracts, and one-off analyses into the warehouse or semantic layer where appropriate.
• Co-author the product with the broader data team, bringing strong ideas and product judgment while working in close partnership with the Head of Data & Analytics and Senior Data Engineer / Data Architect. Navigate competing perspectives and technical tradeoffs constructively, building shared decisions rather than optimizing individual layers of the solution in isolation.
• Build trust into the product through analytical governance, testing, validation, documentation, lineage, metric definitions, and data quality practices. Establish reusable standards and patterns that make analytical products understandable, supportable, and scalable.
• Shape the next generation of the analytical experience, including AI-enabled ways for users to discover, understand, and act on governed enterprise data. Evaluate and evolve the tools, interaction patterns, and analytical capabilities used to deliver that experience as the product and user needs mature.

Ideal Background

• Strong experience in analytics engineering, data modeling, or analytical product development, with the technical depth to work across transformation, semantic modeling, and the user-facing analytics experience.
• Strong SQL skills and hands-on experience with modern cloud data warehouses, ideally Snowflake or similar, and transformation technologies such as dbt, Matillion, DPC, SQL-based ELT, or comparable tools.
• Strong understanding of dimensional modeling, semantic layers, metric governance, and BI consumption patterns, including experience moving reusable business logic out of dashboards and one-off solutions into governed warehouse and semantic-layer models.
• Demonstrated ability to move from business problem to durable analytical solution—understanding what users are trying to accomplish, challenging or refining requirements when appropriate, and influencing stakeholders toward solutions that balance immediate needs with a scalable product direction.
• Strong product and user instincts, with experience designing for different audiences and the judgment to identify where user needs can be served through common capabilities versus where differentiated experiences are warranted. Experience serving both internal and external users or customers is a plus.
• Exposure to analytical methods beyond traditional BI—such as forecasting, segmentation, statistical analysis, anomaly detection, or optimization—with an interest in expanding how analytics and AI can be incorporated into practical, business-facing data products.
• A collaborative technical partner who can co-author solutions with engineers, analytics leaders, and business stakeholders—bringing a point of view, navigating tradeoffs and competing priorities constructively, and building alignment without requiring complete agreement at the outset.
• Strong discipline around data quality, testing, documentation, definitions, lineage, transformation logic, and assumptions, with a bias toward building trusted, reusable capabilities rather than one-off solutions.