Description
Lead Data Engineer
Department: Technology
Employment Type: Full Time
Location: East Rutherford, New Jersey, East Rutherford, NJ
Who We Are:
APC is a dynamic, growing international delivery and technology solutions provider. Established in 2001, APC has been simplifying international delivery for over 20 years, helping U.S. businesses build brands abroad, expand their consumer base, increase revenue, and their global reach. Our extensive, reliable worldwide delivery network and proprietary technology empower our brands to succeed. We are committed to expanding our footprint and continuously investing in innovative delivery and technology solutions as we aim to be the leading resource for cross-border solutions.
About the Role:
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.
About the Candidate:
About the Candidate:
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.
Key Responsibilities
Data Platform & Warehouse Leadership
- 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.
Data Engineering & Pipelines
- 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.
Analytics Engineering & Business Enablement
- 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.
Applied Data Science & Advanced Analytics
- Develop and product-ionize 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.
Technical Leadership
- 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.
Key Responsibilities:
Data Platform & Warehouse Leadership
- 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.
Data Engineering & Pipelines
- 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.
Analytics Engineering & Business Enablement
- 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.
Applied Data Science & Advanced Analytics
- 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.
Technical Leadership
- 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.
Skills, Knowledge and Expertise
- 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.
- Experience in logistics, e‑commerce, supply chain, fulfillment, or adjacent domains is strongly preferred.
Core Competencies
Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences
Manages Complexity: Making sense of complex, high quantity, and sometimes contradictory information
Business Insight: Applying knowledge of business goals and the marketplace to advance the organization’s goals
Financial Acumen: Interpreting and applying understanding of key financial indicators to make better business decisions
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