Lead Data Engineer

Red Clover HR•East Rutherford, NJ

About The Position

Reporting to the Head of Technology, the Data Architect / Lead Data Engineer will own the design, implementation, scalability, and business impact of APC’s data platform supporting a high‑volume, complex logistics operation. This is a hands‑on, senior technical role responsible for shaping how data flows from raw operational systems through trusted analytics and into production‑grade models that drive decision‑making across the business. You will lead the evolution of the data stack end‑to‑end, balancing architectural rigor with real‑world constraints such as imperfect data, operational urgency, and cost efficiency. Partnering closely with engineering, product, operations, finance, and leadership, you will translate complex business questions into durable data solutions that directly impact shipment performance, customer experience, pricing, and profitability. This role is well‑suited for someone who enjoys deep technical ownership, cross‑functional collaboration, and building systems that operate at scale in production environments. You are a hands‑on, systems‑oriented data leader who enjoys working close to the business and taking ownership of complex, real‑world data challenges. You are comfortable operating at the intersection of architecture, engineering, analytics, and applied data science, and you thrive in environments where data is imperfect, scale is real, and impact is tangible. You bring a strong sense of accountability and technical judgment, with the ability to make thoughtful tradeoffs and move initiatives forward. You communicate clearly with both technical and non‑technical partners, build trust through reliability and transparency, and enjoy mentoring others while setting a high bar for data quality and engineering discipline.

Requirements

8+ years of experience in data engineering, analytics engineering, or applied data science roles, with demonstrated ownership of production data systems.
Proven experience designing and operating a scalable data warehouse or analytics platform in a production environment.
Advanced SQL expertise, including query optimization, indexing and partitioning strategies, dimensional modeling, and slowly changing dimensions.
Strong experience building and maintaining data pipelines using modern data tooling and orchestration frameworks.
Proficiency in Python or a similar language for data processing, modeling, and automation.
Experience working with large‑scale, high‑volume operational datasets and managing data quality in real‑world conditions.
Strong systems thinking and the ability to design for reliability, performance, and cost control.
Demonstrated ability to translate business problems into effective technical solutions.

Nice To Haves

Experience in logistics, e‑commerce, supply chain, fulfillment, or adjacent domains is strongly preferred.

Responsibilities

Own the architecture, scalability, performance, and cost efficiency of the enterprise data warehouse and analytics platform.
Design and maintain dimensional and analytical data models supporting core logistics and financial use cases, including shipments, customs data, carrier performance, SLAs, revenue, and margins.
Establish and enforce best practices for data modeling, schema evolution, data quality monitoring, lineage, documentation, and cost governance.
Lead data platform migrations, upgrades, and modernization initiatives as needed.
Design, build, and operate reliable, observable ELT/ETL pipelines from internal systems, third-party APIs, and event streams, including change data capture where required.
Ensure pipeline resilience through strong handling of data freshness, schema drift, backfills, reprocessing, and failure recovery.
Integrate data across core operational platforms, including OMS, WMS, TMS, payments, CRM, and marketing systems.
Partner with application and platform engineers on event design, instrumentation, and data contracts.
Build and maintain trusted semantic layers, metrics definitions, and curated data marts for analytics, operations, finance, and executive reporting.
Define and operationalize canonical KPIs across shipment performance, carrier reliability, exceptions, margins, and customer lifecycle metrics.
Enable governed self-service analytics while preserving data accuracy, consistency, and trust.
Develop and productionize analytical models for forecasting, operational optimization, anomaly detection, and customer behavior analysis.
Design experiments and analyses with clear business impact and measurable ROI, and operationalize outputs into dashboards or workflows.
Serve as technical owner for data architecture, tooling selection, and platform standards.
Mentor engineers and analysts, lead design reviews, and set pragmatic standards balancing speed, reliability, and cost.