Data Engineering Lead

Future Works

2d • Remote

About The Position

As the Data Engineering Lead, you will build the critical data foundation that makes this transformation possible. You will be accountable for extracting and unifying highly fragmented historical logistics data to power advanced AI simulations. You will work closely with Solutions Architects, Business Analysts, and Full Stack Engineers to establish a secure environment and a canonical schema that translates raw supply chain data into insights that drive speed, efficiency and fiscal gain.

Requirements

• 5+ years of senior-level data engineering experience with a proven track record of designing data architecture, data lakes/warehouses, and complex ETL/ELT pipelines.
• Technical Mastery: Deep proficiency in Python, advanced SQL, and modern data processing libraries
• Cloud Expertise: Hands-on experience working within cloud environments to deploy secure, high-performance data infrastructure (e.g., PostgreSQL).
• Agile Execution: Ability to thrive in rapid, hypothesis-driven sprint cycles (12-week models) where the focus is on validation experiments rather than traditional, slow IT delivery.
• AI-Native Workflow: Comfort utilizing LLM code assistants (e.g., Cursor, Copilot) and agentic engineering to multiply your productivity and enhance code quality.

Nice To Haves

• Domain Knowledge: Previous experience working with supply chain, logistics, or ERP data (shipment histories, inventory levels, freight billing) is highly preferred.

Responsibilities

• Data Ingestion & Structuring: Lead the extraction, ingestion, and harmonization of historical freight, order, inventory, and complex carrier rate card data from diverse legacy sources.
• Schema Design: Define "Minimum Viable Attributes" and architect a unified, canonical data schema that normalizes client data for downstream consumption.
• Pipeline Development: Build robust, reusable ETL/ELT pipelines to facilitate rapid and structured analysis in a highly regulated environment.
• Quality Assurance for Modelling: Ensure data quality, completeness, and readiness are sufficient to train predictive machine learning models and run optimization simulations.
• Infrastructure Collaboration: Work alongside the Solutions Architect to stand up and operate within a secure, single-tenant sandbox environment ensuring strict data isolation.
• Hypothesis Validation: Support the Operations Strategy team by providing the necessary data structures to decompose current allocation logic, enabling the identification of wrong-node, mode-mix, and escalation drivers.

Benefits

• Work from anywhere, forever - We are a fully remote and global team. We trust you to manage your time and energy to deliver exceptional results.
• Connect deeply - We gather for immersive, all-expenses-paid company retreats in unique locations to connect, learn, and grow together.
• Share in the upside - A competitive compensation package including equity, bonuses, substantial participation in company profits with a clear growth path to C-Level leadership based on performance.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed