Data Engineering Team Lead

Description

About Us

Hello, we’re Flare! We are reimagining how attorneys and clients work together. We are obsessed with building solutions that empower attorneys and consumers through the legal journey. Funded by leading VC firms, we are an industry leader in legal technology. Our platform simplifies the delivery of complex legal services while providing ease and clarity to clients and attorneys throughout the process. With a global team that brings a fresh perspective, Flare is revolutionizing the way legal services are delivered. We’re a team of creative problem-solvers who want to make the world a better place. Join us.

Check out helloflare.com for more!

Overview

At Flare, reliable and scalable data infrastructure isn’t just a technical goal - it’s central to our ability to power AI-driven innovation and enrich our product offerings. As Data Engineering Team Lead, you’ll combine hands-on technical expertise with people-first leadership. You’ll own the strategy, architecture, and delivery of our Production Data Layer Infrastructure and guide a growing team of data engineers to build the foundations that enable data-powered product development across the company.

What You’ll Do

• Set Vision & Roadmap: Define the data engineering strategy in partnership with product, AI, and platform leads.
• Stay Hands-On: Lead critical design reviews and own complex components (~80% coding), troubleshooting as needed.
• Lead & Grow Team: Manage, hire, and mentor a high-performing team, driving career growth and accountability.
• Own the Data Platform: Build and scale real-time and batch infrastructure with strong SLAs and compliance.
• Ensure Quality: Establish standards for modeling, observability, orchestration, and governance.
• Collaborate Cross-Functionally: Partner with backend, platform, AI/ML, and product teams to deliver data-powered features.
• Drive Strategy: Shape architecture to support Flare’s AI goals and long-term scalability.
• Champion Excellence: Promote data best practices and foster a culture of ownership and improvement.
  
  

• 7+ years of professional experience in data engineering, with 2+ years leading or managing engineers.
• Proven track record of building and operating production-grade data platforms (streaming and batch) using technologies such as Kafka, Flink, Airflow, dbt, Snowflake, PostgreSQL, ClickHouse, or equivalents.
• Advanced proficiency in SQL and Python.
• Demonstrated ability to set technical direction, prioritize roadmaps, and deliver results through others.
• Strong understanding of data architecture, reliability, scalability, and best practices for AI/ML pipelines.
• Excellent communication and collaboration skills; comfortable influencing senior stakeholders and working cross-functionally.
• Bachelor’s or Master’s degree in Computer Science, Information Systems, or equivalent experience.
  
  

• Experience transitioning from monolithic data platforms to Data Mesh or decentralized data ownership models.
• Background in implementing data governance frameworks or compliance (e.g., GDPR, CCPA).
• Prior success leading teams that built platforms supporting AI/ML workloads end-to-end.
• Familiarity with infrastructure-as-code (e.g., Terraform) and cloud-native data services (AWS, GCP, or Azure).