Data Engineering Team Lead

Description About Port At Port.io, we are building an open and flexible Agentic Engineering Platform for modern engineering organizations. Following our recent $100M Series C funding round, we are in a phase of rapid hypergrowth with strong enterprise momentum. We act as the central nervous system for engineering, enabling platform teams to unify their stack and expose it as a governed layer through golden paths for developers and AI agents. By combining rich engineering context, workflows, and actions, we help organizations transition from manual processes to autonomous, AI-assisted engineering workflows while maintaining control and accountability. As a product-led company, we believe in building world-class platforms that fundamentally shape how modern engineering organizations operate. Why we’re looking for you: We’re looking for a Data Engineering Team Lead to take ownership of Port’s data engineering team and help shape the future of agentic engineering portals. This is a hands-on leadership role: you’ll guide a team, drive technical direction, and help establish Port’s data warehouse both for product use cases and for internal business analytics. What you’ll do: At Port, we’re building a platform by developers, for developers. As a Data Engineering Team Lead, you’ll balance leadership and data engineering work - setting technical direction while enabling your team to thrive. Your responsibilities will include: Lead the design and development of scalable and efficient data warehouse and BI solutions that align with organizational goals and requirements. Utilize advanced data modeling techniques to create robust data structures supporting reporting and analytics needs. Implement ETL/ELT processes to assist in the extraction, transformation, and loading of data from various sources into a shared data warehouse. Identify and address performance bottlenecks within our data warehouse, optimize queries and processes, and enhance data retrieval efficiency. Develop and provide a data backend for product facing features such as usage analytics, data insights and enabling an agentic context lake Collaborate with cross-functional teams (product, R&D, analysts) to deliver actionable data solutions tailored to their needs. Who you’ll work with: You’ll collaborate with R&D teams to understand existing data sources, determine where data lives and how to best model a data warehouse that acts as the base of the company’s analytics and reporting. You’ll work alongside product data analysts to generate meaningful insights from product usage data and help steer future product decisions. Requirements 2+ years of experience leading a team, balancing people management with hands-on technical leadership. 5+ years of experience in Data Engineering, designing and operating scalable data platforms and pipelines. Strong expertise in data warehousing, data modeling (including dimensional modeling and SCDs), and building ETL/ELT pipelines at scale. Hands-on experience with modern data platforms and tooling, including technologies such as Snowflake, Databricks, BigQuery, Redshift, Airflow, dbt, Fivetran, Airbyte, or similar. Expert-level SQL skills and experience working with large-scale datasets, including CDC-based architectures and NoSQL databases. Strong software engineering skills, including experience with Python and modern backend development practices. Experience building reliable, governed, and high-quality data systems, including data quality, governance, semantic layers, and metric definitions. Experience enabling analytics and business decision-making through BI and reporting tools such as Tableau, Looker, Metabase, or Qlik. Strong analytical thinking and the ability to translate complex data into actionable insights for technical and business stakeholders. Proven ability to collaborate effectively with product managers, analysts, engineers, and business stakeholders to deliver end-to-end data solutions. Excellent communication and documentation skills. Fluent Hebrew and English. Nice to have: Experience with streaming and real-time data technologies such as Kafka or Kinesis. Experience with cloud-native infrastructure, containerization, and orchestration technologies such as Docker and Kubernetes. Experience with Node.js, TypeScript, or Golang. Experience building data infrastructure that supports AI, machine learning, or agent-based products.