Face to face interview for Senior Analytics Engineer / Senior Data Analyst 2 @ Houston TX--Hybrid

Saxon Global•Houston, TX

5d•Hybrid

About The Position

This team is responsible for delivering analytics and insights across Supply Chain, Cybersecurity, and Enterprise Risk Management. The team is undergoing significant modernization as they build the Helix Data Platform on Google BigQuery, with a strategic shift away from legacy BI report centric analytics (Tableau/Power BI) and toward AI enabled, semantic layer driven insight generation. Due to a recent reorganization and attrition, the team is down approximately six headcount compared to last year. Skillsets on the current team are also not aligned with the future state of the program. To support this transformation, the team needs at least two Senior Analytics Engineers with strong data science and AI foundations who can serve as independent, consultative problem solvers. This position directly impacts the modernization of the client's enterprise analytics ecosystem, enabling the organization to leverage AI driven insights, improve operational efficiency, and accelerate the transformation of critical business functions. This role will focus heavily on building the semantic data asset layer that powers the Helix platform, enabling downstream AI agents, LLM powered search, and reusable enterprise logic. The ideal candidate has deep SQL, semantic modeling, and practical AI knowledge — with enough Python to automate and augment analytical workflows. While not responsible for building new Tableau dashboards, this role will reverse engineer existing Tableau/Power BI logic and rebuild it as reusable BigQuery based semantic components designed for both traditional analytics and AI/LLM use cases.

Requirements

Bachelor's Degree (mandatory for conversion).
7+ years of analytics, data modeling, or analytics engineering experience.
Very strong SQL skills — ability to build complex data models and logic layers.
Python scripting for data preparation and workflow automation.
Semantic data modeling experience (warehouse centric or metric layer centric).
Understanding of BI tools (Tableau/Power BI) to translate existing logic into semantic components.
AI/LLM knowledge, including: Prompt engineering, Foundation model selection (Gemini or equivalent), Dataset preparation for LLM tuning or fine tuning
Experience with Google BigQuery, Gemini, or equivalents on other cloud platforms (AWS Redshift/Bedrock, Azure OpenAI).
Strong interpersonal and communication skills; ability to work directly with business and IT stakeholders.

Nice To Haves

Experience supporting Supply Chain, Cybersecurity, or Enterprise Risk data domains.
Familiarity with cloud native AI tools or agentic frameworks.
Prior consulting experience or roles requiring high autonomy.

Responsibilities

Build robust semantic data models within Google BigQuery, forming the foundational data asset layer for the Helix platform.
Reverse engineer business logic currently embedded in Tableau/Power BI reports and reconstruct it as centralized semantic layer logic.
Ensure semantic structures support AI consumption, including LLM context windows, training datasets, and agentic workflows.
Apply practical AI skills: Understanding of LLM behavior, training and tuning considerations, and dataset preparation.
Working with Gemini foundation models and selecting appropriate models for each use case.
Performing prompt engineering for optimized LLM output.
Partner with data engineers and product teams to ensure datasets and semantic structures are ready for AI agents, generative search, and NLP-driven insights.
Write advanced SQL for data modeling, transformations, and logic reconstruction.
Use Python for lightweight data processing, scripting, and automation.
Perform small scale ingestion of ad hoc sources (Excel, SharePoint) when needed.
Work collaboratively with IT stakeholders, business users, and product owners in Supply Chain, Cybersecurity, and Enterprise Risk.
Lead conversations, clarify requirements, and independently drive analytical problem solving.
Act as a consultant style contributor: proactive, self directed, and comfortable navigating ambiguity.
Partner closely with the Data Engineering team, who owns large scale pipelines and ingestion.
Understand pipeline capabilities and limitations and know when to handle smaller ingestion or transformation tasks directly.
Understand Tableau/Power BI concepts sufficiently to trace logic embedded in reports.
Rebuild that logic into the BigQuery semantic layer that Helix will utilize.