Director – Data Engineering

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POLYWOOD®

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1990

🏭 Manufacturing

🛍️ eCommerce

👥 B2C

Manufacturing • eCommerce • B2C

POLYWOOD is a U. S. -based maker of durable, low-maintenance outdoor furniture engineered from proprietary polymer lumber and weather-resistant materials. Founded in 1990 and manufactured in facilities in Indiana and North Carolina, POLYWOOD offers Adirondack chairs, dining sets, deep seating, planters, in-pool furniture and accessories, marketed directly to consumers through its website and showrooms. The company emphasizes sustainability and long-lasting performance with product technologies and warranties (ClimateTuff™, Solidcore™, ColorStay™, POLYWOOD Select™) that resist splintering, rot, fading, and extreme weather.

📋 Description

• Design and implement a scalable lakehouse medallion architecture within Databricks. • Establish ingestion, canonical, and business-ready data modeling layers with clear standards and naming conventions. • Build and maintain structured, governed fact and dimension models for enterprise reporting. • Oversee ingestion pipelines using Fivetran, Airbyte, and custom integrations while optimizing cost and performance. • Develop and manage SQL and Python transformation jobs including incremental processing, upsert logic, and snapshot modeling. • Implement orchestration, monitoring, data quality validation, and failure recovery processes. • Define enterprise data governance standards including access controls and reconciliation processes. • Integrate ERP, Shopify, GA4, Google Ads, Meta Ads, and catalog marketing data into unified customer and order models. • Lead and mentor a small team of data and analytics engineers while setting coding and review standards.

🎯 Requirements

• 7+ years of experience in data engineering, analytics engineering, or data architecture. • 3+ years leading data platform or warehouse initiatives. • Expert-level expertise in Databricks and medallion architecture design. • Advanced SQL proficiency and strong Python experience for data transformations. • Experience designing fact/dimension models and implementing incremental pipelines and snapshot logic. • Strong understanding of data governance, access control, and quality frameworks. • Experience integrating commerce, finance, ERP, and marketing data sources into structured models. • Strong communication skills and the ability to collaborate across technical and business teams.

🏖️ Benefits

• Valued Contribution: Build the foundational data infrastructure that powers enterprise decisions. • Growth Opportunities: Shape standards, architecture, and long-term data strategy. • Team Environment: Partner with business leaders and analytics teams to deliver trusted insights. • Purpose Driven Work: Enable scalable analytics that strengthen operations, finance, and commerce performance.