Senior BI Engineer

Hey, we’re amp, and we’re leading the fitness evolution.

Forget the guesswork, the clutter, and the compromises—amp brings full-body, AI-powered workouts straight to your home. With intelligent resistance, expert-led programs, real-time progress tracking, and a sleek, space-saving design, we make high-performance training part of everyday life.

Our Mission? 

To help people live longer, stronger lives by making strength training effortless, effective, and built around you.

We’re a team of 120+ passionate minds—designers obsessed with every detail, engineers solving real problems, and training experts who know that consistency beats intensity. We’ve brought together talent from Apple, DraftKings, Nike, Spotify, and Meta, all with one goal: redefining strength training from the ground up. amp isn’t just a product—it’s a new standard.

We built amp because strength training should fit your life, not the other way around.

Where We Are Now?

Over the past four years, we’ve developed a next-generation strength training system designed for personalized, intelligent workouts. After a successful soft launch, we’re scaling fast with a cutting-edge D2C e-commerce platform built for massive growth.

Now, we’re looking for top-tier talent to help shape the future of strength training.

Are you in? 

We’re looking for an BI / Analytics Engineer to build and own the backbone of our data ecosystem.

You’ll design and manage our dbt + Snowflake transformation layer, model clean and governed semantic datasets, and make reliable insights available across the business.

If you’re passionate about transforming raw data into trustworthy intelligence that drives real decisions, this role is for you.

You’ll collaborate closely with Product, Finance, Operations, and Engineering teams to turn business questions into reliable, well-documented datasets that everyone can trust.

This role sits at the intersection of data engineering, analytics, and enablement, shaping how data flows and informs decisions across the company.

Data Modeling & Transformation

• Design, build, and maintain dbt projects across staging, intermediate, and mart layers.
• Apply dimensional modeling principles (clear grain, conformed dimensions, SCD strategies) and evolve schemas through data contracts.
• Publish analytics-ready datasets with governed metrics and semantics that power BI and AI systems.

⚙️ Data Quality, Governance & Reliability

• Implement data quality frameworks with dbt tests, freshness checks, and lineage.
• Establish and maintain SLAs, incident playbooks, and documentation for all core datasets.
• Embed CI/CD and Git-based workflows to enforce code quality and reproducibility.

? Analytics Enablement

• Partner with analysts and business stakeholders to deliver dashboards, reports, and alerting through BI tools.
• Drive self-serve analytics by defining metrics and maintaining a semantic layer as a single source of truth.
• Advocate for consistent data definitions, formatting, and visualization standards across teams.

? Performance & Optimization

• Tune Snowflake performance and cost efficiency: warehouse strategy, clustering, pruning, caching, and monitoring.
• Develop Python utilities for ingestion, validation, and automation around dbt/Snowflake workflows, amp Careers typescript 
• Integrate new data sources (APIs, files, streams) with partitioning, metadata, and schema evolution.

? AI & Automation Readiness

• Structure data for retrieval-augmented generation (RAG) and internal knowledge assistants.
• Expose well-documented metadata to enable AI-driven workflows and intelligent automation across teams.

• 3–5+ years as a BI/Analytics Engineer with end-to-end ELT ownership and data modeling (must).
• Expert SQL and production dbt (Core/Cloud).
• Proficient in Python for automation, ingestion, and validation.
• Snowflake expertise — performance tuning, security, and cost management.
• Proven delivery of production-grade datasets and BI artifacts; strong semantic/metrics layer practice.
• Salesforce/CRM data experience: syncing (e.g., via managed connectors), object modeling, de-duplication/identity resolution, and revenue metrics alignment.
• Proven delivery of production datasets and BI artifacts, ideally with a mature semantic layer practice.
• Experience integrating unstructured/semi-structured data (JSON, Parquet, logs).
• Strong communication skills; able to translate technical concepts for non-technical audiences.
• Experience designing data for AI agents, RAG pipelines, or workflow automation.

• Familiarity with orchestration tools (Airflow, Rivery, Fivetran) or CI/CD pipelines for analytics.
• Background in fitness, health tech, or IoT/sensor data analytics.
• Typescript Advantage