Sr. Data Analytics Engineer

Fabletics•California, CA

2d•$120,000 - $140,000•Remote

About The Position

FableticsOS is looking for a Senior Data Analytics Engineer to strengthen the data foundations of our Global Supply Chain and Merchandise Planning domains. You will model and build the data that powers how we move product around the world and decide what to make and buy — connecting logistics, sourcing, ERP, and planning systems into trusted, reusable data products. As a senior individual contributor, you will set the technical direction for these data products, make the architectural calls, and be the go-to expert for the planning and supply chain teams who depend on them — giving them a single, reliable view of inventory in motion, freight, and landed costs to make faster, more confident decisions. As a Senior Data Analytics Engineer, you will design and build scalable, high-quality data models and pipelines that support our supply chain and merchandise planning organizations. You'll lead technical design, establish engineering standards, and deliver analytical data products end-to-end—from discovery and architecture through implementation. Partnering closely with planning, sourcing, logistics, and commercial teams, you'll translate business needs into data solutions that improve planning, forecasting, and operational decision-making. This role focuses on two primary domains: Logistics & Supply Chain Visibility – Integrate ETA, transit, freight, and landed-cost data from logistics platforms into Snowflake, combining it with PLM, ERP, and planning data to improve purchase order planning and visibility. Planning Platform Data Architecture – Own the data feeds and integrations that power our supply chain and merchandise planning platform, ensuring data integrity while supporting expansion into assortment, production, and capacity planning. Supporting both areas is a strong foundation in our enterprise data warehouse (EDW), including core models, standards, and architectural principles that underpin all analytics and data engineering work across the organization.

Requirements

6+ years of experience in a data or software-adjacent field, working with systems and data infrastructure at scale.
Experience designing and implementing mature data pipelines across a data lake and Enterprise Data Warehouse (EDW), using Dimensional (Kimball) modeling methodologies.
Experience integrating data across fragmented or disparate source systems into coherent, reliable data products.
Experience with ELT/ETL tooling and workflow orchestration (e.g., Apache Airflow).
Strong experience with relational databases and SQL in an analytical context.
Experience with data exploration, visualization, and data storytelling.
Excellent written and spoken communication, with a proven ability to work with a wide range of both technical and non-technical stakeholders.
Highly skilled at writing and maintaining high-quality, reusable code, applying design patterns and meeting coding standards.
A pragmatic adopter of AI in your own work — you use LLMs and AI-assisted tooling to accelerate development, code review, documentation, and data exploration, with good judgment about where they help and where they don't.
Track record of independently owning complex, ambiguous data initiatives end-to-end — making the architectural decisions, setting standards, and influencing technical direction across teams.

Nice To Haves

Familiarity with supply chain, logistics, or merchandise planning data and processes (e.g., POs, freight/landed cost, assortment, production, or capacity planning).
Exposure to logistics visibility tools and/or merchandise and supply chain planning platforms.
Ideal candidate brings foundational AI literacy, including experience using generative AI tools, and a proactive mindset to experiment with new technologies to drive productivity and innovation.

Responsibilities

Integrate logistics and planning data: Bring logistics visibility data (ETAs, transit times) into Snowflake and connect it with PLM, ERP, and planning sources for accurate PO loading and freight/landed-cost estimates.
Build trusted, reusable data products: Connect disparate supply chain and merchandising datasets into curated data products across our data lake and EDW, with reliable, well-defined metrics that power self-serve analytics across teams.
Own data quality and integration integrity: Take ownership of data quality across fragmented upstream systems — building the monitoring and controls that keep critical feeds reliable and integrations healthy as the planning landscape evolves.
Model data using best practices: Apply established Data Warehousing methodologies, primarily Dimensional (Kimball) modeling.
Turn complexity into insight: Transform large, complex datasets into actionable insights, delivered in a consumable format for historical and predictive analysis.
Perform data governance: Handle technical stewardship, data classification, compliance, and security considerations.
Validate before release: Build data visualizations to trial and validate data products before releasing them to customers.