Senior Analytics Engineer - Business Intelligence

Torc Robotics•Fort Worth, TX

4d•$160,800 - $193,000•Remote

About The Position

Torc’s Business Intelligence team partners across the company to bring visibility and reporting that help leadership and every function make faster, better-informed decisions. We are looking for a senior technical leader who can own the data models and dashboards that power BI across the business — building the governed, reusable layer that sits between raw source systems and providing consistent reporting in all dashboards used by our stakeholders in different business units. This role sits at the intersection of data engineering and business intelligence. You’ll design and build the pipelines and data models that make reporting fast, trustworthy, and consistent, while also staying close enough to the business to know what leadership actually needs to see. You’ll be a hands-on technical lead for the BI function with a focus on engineering, safety, and operational data — setting standards for data modeling, validation, and reporting, while also mentoring analysts and engineers so the whole team levels up.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Data Analysis, or a related field with 6+ years of experience OR a Master's degree with 3+ years of experience.
Deep hands-on experience with data modeling and database/warehouse design — building governed, reusable data models that serve multiple downstream dashboards and reporting tools consistently.
Strong SQL skills and proficiency in Python (pandas, PySpark, or similar) for pipeline development, transformation, and analysis.
Hands-on experience building and maintaining source-of-truth ETL/ELT pipelines, including data quality checks and validation processes, in production environments.
Experience with one or more BI tools (Tableau, Power BI, Looker, QuickSight, or similar), including enough hands-on dashboard-building experience to speak the language of BI analysts and evaluate their work.
A track record of mentoring or leading other engineers or analysts — through reviews, pairing, documentation, or setting team standards.
Comfort working with ambiguous, evolving requirements, and the ability to partner directly with stakeholders across the business to turn loosely defined problems into well-structured data models.
Strong communication skills and the ability to document and explain technical decisions to both technical and non-technical audiences.
Experience working in an agile environment and balancing multiple concurrent priorities.
Practical experience using AI-assisted coding and productivity tools (e.g., Copilot, Cursor, ChatGPT/Claude) to improve development speed, paired with the judgment to critically review and validate AI-assisted output.

Nice To Haves

Experience with modern cloud data platforms (Databricks, Snowflake, or similar), particularly for serving data, visualizations, and jobs.
AWS experience, particularly Athena, Glue, or other managed data services.
Knowledge of relational, NoSQL, vector, and data warehouse/lakehouse architectures.
Experience with a transformation framework such as dbt, or building analogous version-controlled, tested data modeling practices.
Experience scaling data for ML/AI workloads.
Experience designing semantic/metrics layers or data models that support AI-powered or natural-language BI querying, and familiarity with applying LLMs to tasks like data quality checks, anomaly detection, or documentation generation.
Background in automotive, AV, robotics, or other safety-critical data environments.

Responsibilities

Design, build, and own the data models that serve as the single source of truth for BI reporting — structuring tables, views, and semantic layers so metrics are defined once and used consistently across all dashboards and tools.
Design, build, and maintain ETL/ELT pipelines that feed those models, ensuring data is accurate, timely, and reliable at scale.
Partner with data producers and business stakeholders to translate reporting and decision-making needs into durable data models and pipelines, rather than one-off dashboard logic.
Define, instrument, and maintain business KPIs from scratch using SQL and Python, and ensure definitions stay consistent across teams and dashboards.
Build and enforce data quality checks, validation processes, and documentation so stakeholders can trust the numbers behind leadership- and board-level reporting.
Create and maintain actionable, user-friendly dashboards and visualizations (Tableau, Power BI, or similar) to provide valuable insight.
Support and help evolve leadership-critical reporting deliverables, ensuring governed, well-documented data models rather than ad hoc queries.
Help evolve the team’s data platform and architecture (e.g., improving legacy pipelines), balancing pragmatism with long-term maintainability.
Work with IT and data partners to design secure, sustainable, and scalable paths for data access from source systems into BI-ready models.
Mentor team members across data engineering, data analysis, data validation, and BI reporting — through code and model reviews, pairing, documentation, and by setting reusable standards and best practices.
Cultivate data literacy across the organization through documentation, training, and clear communication of how and why data is modeled the way it is.
Answer complex business questions via ad hoc SQL or Python analysis when a question does not yet warrant a permanent model or dashboard.
Leverage AI-assisted development tools (e.g., GitHub Copilot, Cursor, ChatGPT/Claude) to accelerate SQL, Python, and documentation work, applying sound engineering judgment to review, validate, and take ownership of any AI-generated output before it reaches production.
Evaluate and, where it adds value, implement AI/LLM-powered BI capabilities — such as natural-language querying, semantic layers, and AI-generated summaries or anomaly alerts — to keep our reporting stack modern and to help the team work more efficiently.