Afresh - Data Engineering Manager

• Lead and grow a team of data engineers, providing mentorship, technical guidance, and career development support. • Own execution of customer integrations across multiple product lines (Store Ordering, RecsAPI, DC, Production Planning, and more), ensuring integrations are delivered on time and at a high quality bar. • Build core dataflows that power new Afresh products, working closely with Product and ML teams to understand requirements and deliver reliable data foundations. • Drive integration velocity: improve the speed and predictability of getting new customers from raw data landing to production-ready, leveraging AI tooling, standardized architecture, and strong contractor enablement. • Continuously improve integration processes and shared code: identify patterns across customer integrations and invest in abstractions, templates, and automation that reduce per-customer effort over time. • Collaborate deeply with Solutions Engineers, Product Managers, and customer-facing teams to scope integration work, unblock dependencies, communicate progress, and ensure integrations meet our customers’ needs. • Improve data quality and pipeline reliability by investing in better alerting, self-healing patterns, and resilience to messy or incomplete customer data. • Champion AI-forward engineering practices: evaluate and adopt AI tools and agentic workflows that accelerate development, automate repetitive tasks, and push the team to the bleeding edge of modern data engineering. • Contribute technically: reviewing code and architecture decisions, pairing with engineers on complex problems, and staying close to the work. • What Makes You a Great Fit • We encourage all highly-qualified candidates to apply, even if they don't meet every listed qualification. • 3+ years of engineering management experience • engineering management • Strong technical background in data engineering: experience with Python, PySpark, SQL, dbt, Airflow, and modern data platforms (Databricks, Snowflake, or similar). • data engineering • Track record of shipping high-quality data integrations or ETL systems at scale, and a deep understanding of what makes data pipelines reliable. • data integrations or ETL systems • Experience managing execution across multiple concurrent projects with different customers or stakeholders, balancing competing priorities with clear communication. • Demonstrated ability to improve team velocity through better tooling, process improvements, or automation. • Genuine enthusiasm for AI-augmented engineering workflows. You've experimented with AI coding tools, agentic workflows, or similar approaches, and you have a vision for how they can transform data engineering and uplevel the team. • enthusiasm for AI-augmented engineering workflows • Comfort working with messy, real-world data from enterprise customers, and the pragmatism to ship solutions that work without over-engineering. • Strong collaboration skills: you're effective working across engineering, product, and customer-facing teams in a fast-paced environment.