PIM Engineering Lead

Lake Fusion is seeking a hands-on PIM Engineering Lead to drive the technical implementation of our Product Information Management product. This is a senior/principal-level role combining deep technical execution with engineering leadership — you'll write production code daily while guiding architecture and technical decision-making across the PIM product.

PIM extends Lake Fusion's MDM platform into governed product data management. The product centers on a taxonomy-driven attribute system with inheritance, provenance tracking, and complex hierarchical relationships — making strong data modeling intuition and experience with taxonomy and graph data structures critical to success.

You'll partner closely with Product, engineering leadership, and the offshore engineering team to translate product vision into technical reality. You'll own architectural decisions, set technical direction, mentor engineers, and ship the PIM product.

• Lead PIM technical architecture
  :
  Own end-to-end technical design for the PIM product, including schema design, taxonomy data structures, attribute inheritance, provenance tracking, and integration with Lake Fusion's MDM core.
• Write production code
  :
  Stay hands-on-keys. Build core PIM backend services, data models, and critical pipelines yourself — this is not a management role.
• Guide technical decisions
  :
  Set technical direction for the PIM engineering team, review designs, unblock engineers, and ensure implementation aligns with the broader Lake Fusion architecture.
• Own taxonomy and hierarchy modeling
  :
  Design and evolve data structures for managing large, deeply nested product taxonomies, category hierarchies, attribute inheritance chains, and governed attribute systems.
• Mentor engineers
  :
  Raise the bar across the PIM team through code reviews, design sessions, and pairing — particularly with the offshore engineering team.

Partner with Product
:
Work directly with product management to scope features, make tradeoffs, and deliver a product that handles real-world enterprise product catalogs.

• 8+ years of software or data engineering experience
  , with a strong track record of hands-on technical leadership at the senior or principal level.
• Deep data expertise
  :
  Strong data modeling skills with production experience designing complex relational schemas, hierarchical data structures, and EAV-style patterns.
• Experience managing large product taxonomies or similar taxonomy/ontology/graph data structures in production — this is a strong preference. Think: hierarchical category systems, attribute inheritance, polymorphic entity models, or graph-backed knowledge systems.
• Strong Python skills for backend service development, with SQL Alchemy or similar ORM experience.
• Databricks or Apache Spark experience
  :
  You'll need to reason about large-scale data operations and integration with the broader lakehouse platform.
• Production SQL expertise
  , including query optimization, indexing strategy, and transactional design.
• Proven ability to lead without being a manager
  : drive architectural consensus, mentor engineers, and deliver complex products with distributed teams.

Excellent communication skills
, including the ability to work effectively with an offshore engineering team based in India.

• Direct experience building PIM, MDM, PLM, or catalog management systems.
• Background in retail, manufacturing, CPG, or other industries with complex product data.
• Experience with FastAPI, modern Python async patterns, or Node.js.

Lake Fusion is the modern Master Data Management (MDM) company. Global enterprises across industries ranging from retail to manufacturing and financial services rely on the Lake Fusion platform to unify, govern, and deliver trusted data entities such as customers, products, suppliers, and employees. Built natively on the Databricks Lakehouse, Lake Fusion creates a single source of truth that powers analytics and AI.

Lake Fusion enables organizations worldwide to accelerate innovation with trusted and governed data.