Note: The job is a remote job and is open to candidates in USA. Torc Robotics is seeking a senior technical leader for its Business Intelligence team. The Senior Analytics Engineer will design and own governed data models, ETL/ELT pipelines, KPIs, data quality processes, and dashboards while partnering with stakeholders, mentoring team members, and advancing AI-powered BI capabilities.
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
Skills
- 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
- 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
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