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.
Requirements
3+ years of engineering management experience
Strong technical background in data engineering: experience with Python, PySpark, SQL, dbt, Airflow, and modern data platforms (Databricks, Snowflake, or similar).
Track record of shipping high-quality data integrations or ETL systems at scale, and a deep understanding of what makes data pipelines reliable.
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.
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.
Tech Stack
Airflow
ETL
PySpark
Python
SQL
Benefits
Collaborative, supportive environment & awesome people :)
Work on challenging, real-world data problems that have a direct and visible impact on customers.
Be part of an engineering culture that's genuinely AI-forward. We want to be on the bleeding edge.
Lead a team at a pivotal inflection point: rapid customer growth means your work directly shapes how Afresh scales.
Afresh provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity/expression, marital status, pregnancy or related condition, or any other basis protected by law.