Data Engineering Manager

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At Angi®, we’ve had one simple mission for 30 years: get jobs done well. We make it happen by connecting homeowners with reliable pros who have the skills they need — and connecting pros with homeowners who have the jobs they want.

• Homeowners have turned to Angi for more than 300 million projects
• 1,000+ home service tasks covered
• 2,800 employees worldwide

Angi® is defining the future of the home services industry, creating an environment where homeowners, pros, and employees benefit from more jobs done well.

For homeowners, our platform is a reliable way to find skilled pros. For pros, we're a reliable business partner who helps them find the winnable work they want, when they want. For employees, we’re an amazing place to call home. We can’t wait to welcome you.

Angi is looking for an Data Engineering Manager with strong leadership, technical depth, and delivery experience to help shape the future of our global data platform. You’ll lead the Data Processing & Storage (DPS) team within the Data Platform Products domain under the Technical Foundations Zone - the group responsible for building, scaling, and evolving the core data infrastructure that powers analytics, experimentation, and machine learning across Angi.

As an Engineering Manager focused on data engineering, you will lead a team of experienced data engineers, owning critical components of our ingestion, storage, and compute layers. You’ll balance hands‑on technical involvement with people leadership - fostering a culture of collaboration, accountability, and technical excellence. You’ll play a key role in building and scaling the foundation of Angi’s unified data platform - where our lakehouse powers analytics and data‑driven products across vast datasets.

The Data Processing & Storage team, within the Data Platform Products focus area under the Technical Foundations Zone, sits at the heart of this mission - ensuring Angi’s data ecosystem remains modern, scalable, and built for the future.

• Lead and mentor a team of experienced data engineers, supporting their growth, performance, and technical development.
• Design and deliver large‑scale, reliable, and cost‑efficient data pipelines and platforms that power analytics, reporting, and data‑driven products.
• Own and evolve the core compute data infrastructure built on Trino, ensuring scalable, performant, and cost‑optimized query processing across the data lakehouse environment.
• Oversee major data migrations from our warehouse systems to modern, cloud‑native lakehouse data platforms with minimal disruption.
• Drive the evolution of our data lakehouse and warehouse ecosystems, optimizing storage, compute, and orchestration for performance and cost efficiency.
• Collaborate closely with Product, Analytics, and Engineering teams to deliver data infrastructure that enables trustworthy and timely insights.
• Champion best practices in data modeling, governance, and observability to ensure data quality, discoverability, and reliability across the organization.
• Contribute to infrastructure and automation, leveraging tools like Terraform, Docker, Kubernetes, and CI/CD pipelines to ensure consistency, scalability, and resilience.
• Stay technically engaged, participating in architectural design, reviewing implementations, and guiding the team through complex technical decisions.
• Leverages AI tools (e.g. Copilot, Cursor, Claude Code) to improve coding speed and debug faster.

• 8+ years of professional experience in data engineering or software engineering, with a deep understanding of distributed data systems and modern data architecture.
• 2+ years of experience managing engineering teams, including performance management and leading senior engineers.
• Proven experience building and operating large-scale, cloud-based data platforms, ideally in AWS (S3, Glue, IAM, CLI, etc.) or equivalent environments.
• Hands‑on experience with SQL and Python, and a strong understanding of ETL/ELT workflows and data lifecycle management.
• Deep experience with data warehouse and data lakehouse technologies such as Snowflake, Redshift, Big Query,…