Staff Data Engineer

Why project44?  

At project44, we believe in better.  

We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential. Better for our customers. Better for their business. Better for the world.  

With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.  

Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose.  

If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk. 

Description:   

project44 is looking for a Staff Data Engineer to join our engineering team. You will work in a fast-paced Agile environment designing, building, and implementing best-in-class integrations to accelerate how project44 connects to the world’s logistics networks.   

About the Role

We are looking for a Staff Data Engineer to drive the execution and scaling of project44’s data platform.This role sits at the intersection of Data Engineering, Data Science, and Product, with a mandate to translate architectural vision into reliable, scalable systems used across teams.

You will partner closely with other data engineers to implement platform direction, establish engineering standards, and ensure consistent, high-quality execution across data systems. Your focus will be on solving complex data challenges, driving adoption of best practices, and enabling teams to build efficiently on a shared foundation.

As AI reshapes how data systems operate, you will also help integrate agentic and GenAI-powered workflows into data pipelines and developer workflows—improving automation, observability, and system intelligence.

What You’ll Do

Drive Platform Execution

• Translate architectural direction into production-grade systems across ingestion, transformation, and serving layers.

Establish Standards & Best Practices

• Define and enforce patterns for data modeling, pipeline design, observability, testing, and data quality across teams.

Solve Complex Data Problems

• Own and resolve high-impact technical challenges related to scale, latency, reliability, and cost efficiency.

Enable Consistency Across Teams

• Work across multiple teams to drive adoption of shared data models, tooling, and engineering practices.

Build Scalable Data Systems

• Develop and optimize pipelines and data systems for performance, maintainability, and long-term scalability.

Integrate AI-Native Workflows

• Implement agentic and GenAI-driven workflows to automate data operations, improve monitoring, and accelerate development.

Mentor & Raise the Bar

• Act as a technical leader within the team, mentoring junior engineers and elevating engineering quality across the organization.

What We’re Looking For

8–10+ years in Data Engineering or Software Engineering, with a strong track record of building and scaling production-grade systems.

Technical Expertise

• Strong programming skills (Python preferred, Java/Scala a plus) and deep experience with modern data technologies (Spark, Snowflake, Databricks).

Data Systems & Execution

• Proven ability to build and operate reliable, scalable data pipelines and systems in production environments.

Data Modeling & Warehousing

• Strong experience in data modeling, warehousing, and building analytics- and ML-ready datasets.

Distributed Systems & Trade-offs

• Ability to navigate trade-offs across performance, cost, reliability, and scalability in cloud-native systems.

AI & Modern Tooling

• Experience leveraging GenAI or agentic tools to improve developer productivity, automate workflows, or enhance data systems.

Cross-Team Influence

• Experience working across teams to drive alignment, adoption of standards, and consistent execution.

Communication & Leadership

• Strong communication skills and the ability to influence technical decisions across teams.

Preferred Skills

• Experience with real-time and event-driven systems (Kafka, streaming architectures)
• Familiarity with orchestration tools (Airflow, Argo) and containerization (Docker, Kubernetes)
• Experience with data governance and access control (RBAC)
• Exposure to analytics and BI tools (Looker, Tableau)
• Experience building shared data platforms or internal tooling
• Background in logistics, supply chain, or high-scale operational systems

What Success Looks Like (6–12 Months)

• Successfully implemented core data platform patterns across multiple teams
• Improved data reliability, observability, and quality in critical pipelines
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