Profitmind is building the intelligence behind how retailers make pricing and merchandising decisions. Today, many of these decisions are still driven by spreadsheets, rigid rules, and manual judgment, even at the largest brands.

Our platform turns complex data such as sales, inventory, and competitive signals into clear, explainable recommendations merchants can trust. Our platform focuses on impact, helping retailers improve margin, inventory health, and decision quality at scale.

Based in Pittsburgh, we’re a fast-growing startup with real customers. Profitmind is backed by a recent strategic investment from Accenture, and scaling its agentic AI platform to power decision-making for some of the world’s largest retailers.

About the role:

We're seeking a talented Senior Data Engineer to build and scale the data infrastructure that powers our retail technology platform. As a key member of our early-stage team, you'll design and implement robust data pipelines, establish best practices for data engineering, and enable data-driven decision making across the organization. This is a unique opportunity to shape the data foundation of a growing startup and work with cutting-edge technologies in both batch and streaming data processing.

What you’ll do:

• Design and implement scalable data pipelines for both batch and streaming workloads using Python and Spark
• Build and maintain cloud-native data infrastructure to support our retail analytics and operational needs
• Develop real-time streaming solutions to process transaction, inventory, and operational data
• Develop AI agents to automate operations
• Implement data quality validation and monitoring to ensure reliability across our data ecosystem
• Establish DataOps best practices and automation as we scale our data infrastructure Own data security and privacy compliance as we handle sensitive customer and business data
• Collaborate closely with product, engineering, and business teams to understand data requirements and deliver solutions
• Mentor team members and contribute to building a strong data engineering culture

What we’re looking for:

• 5+ years of hands-on data engineering experience
• Strong proficiency in Python for data processing and pipeline development
• Production experience with Apache Spark for large-scale data processing
• Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure)
• Proven track record implementing both batch and streaming data pipelines
• Deep understanding of SQL and modern data warehouse technologies
• Strong knowledge of data modeling, ETL/ELT patterns, and workflow orchestration Experience with distributed systems and cloud infrastructure
• Excellent problem-solving and analytical skills
• Strong communication skills with ability to translate business needs into technical solutions Thrives in fast-paced, early-stage startup environments with changing priorities

Nice to have:

• Experience in retail tech, e-commerce, or similar enterprise systems
• Expertise with real-time processing frameworks like Kafka, Kinesis, or Flink
• Proficiency with Python data handling libraries (pandas, NumPy, polars)
• Knowledge of data quality and observability platforms
• Understanding of event-driven architectures, medallion architectures, and CDC patterns
• Previous experience at an early-stage startup or building data infrastructure from scratch
• Background working with multi-tenant SaaS platforms or enterprise analytics systems
• Experience with container orchestration (Docker, Kubernetes)
• Knowledge of DataOps and MLOps best practices

Apply today to join a small, senior team working on problems that sit at the core of how global retailers operate.