Required Data Engineering Tech Lead
Data is everythingits at the heart of every product decision, every customer insight, and every strategic move we make. The Data Group builds and maintains the core data assets that power our products, from our Company and Contact databases to our Connect and Recommendation systems. This role sits at the forefront of that mission, leading engineering innovation that directly shapes the experience of our growing user base and drives the evolution of our intelligent data services.
What will you be responsible for?
Leading the design and evolution of scalable, high-quality data workflowsfrom batch pipelines to real-time data products.
Setting and upholding engineering best practices across code quality, testing, CI/CD, observability, and documentation.
Mentoring and empowering data engineers while fostering a high-performance, collaborative team culture.
Identifying and executing on opportunities to create new data features and assets that enhance product capabilities.
Driving architectural decisions around data modeling, storage, and compute in cloud environments (e.g., Databricks, Snowflake).
Collaborating with cross-functional stakeholdersincluding Product, DevOps, and R&Dto ensure seamless delivery and platform excellence.
Championing a data-driven mindset throughout the organization, balancing business context with technical rigor.
Data is everythingits at the heart of every product decision, every customer insight, and every strategic move we make. The Data Group builds and maintains the core data assets that power our products, from our Company and Contact databases to our Connect and Recommendation systems. This role sits at the forefront of that mission, leading engineering innovation that directly shapes the experience of our growing user base and drives the evolution of our intelligent data services.
What will you be responsible for?
Leading the design and evolution of scalable, high-quality data workflowsfrom batch pipelines to real-time data products.
Setting and upholding engineering best practices across code quality, testing, CI/CD, observability, and documentation.
Mentoring and empowering data engineers while fostering a high-performance, collaborative team culture.
Identifying and executing on opportunities to create new data features and assets that enhance product capabilities.
Driving architectural decisions around data modeling, storage, and compute in cloud environments (e.g., Databricks, Snowflake).
Collaborating with cross-functional stakeholdersincluding Product, DevOps, and R&Dto ensure seamless delivery and platform excellence.
Championing a data-driven mindset throughout the organization, balancing business context with technical rigor.
Requirements:
5+ years of hands-on experience building and maintaining large-scale data pipelines (batch and streaming).
Deep expertise in Python and SQL, with a focus on clean, reliable, and performant code.
Strong problem-solving mindset with the ability to break down complex technical challenges and align solutions to business goals.
Solid background in data modeling, analytics, and architecture design for scalability and cost-effectiveness.
Experience working with modern OLAP systems and cloud data platforms like Databricks, Snowflake, or BigQuery.
Nice to have: familiarity with AI agent protocols (e.g., A2A, MCP) and LLM-based technologies (e.g., vector databases, embeddings) for intelligent data applications.
AI-savvy: comfortable working with AI tools and staying ahead of emerging trends.
5+ years of hands-on experience building and maintaining large-scale data pipelines (batch and streaming).
Deep expertise in Python and SQL, with a focus on clean, reliable, and performant code.
Strong problem-solving mindset with the ability to break down complex technical challenges and align solutions to business goals.
Solid background in data modeling, analytics, and architecture design for scalability and cost-effectiveness.
Experience working with modern OLAP systems and cloud data platforms like Databricks, Snowflake, or BigQuery.
Nice to have: familiarity with AI agent protocols (e.g., A2A, MCP) and LLM-based technologies (e.g., vector databases, embeddings) for intelligent data applications.
AI-savvy: comfortable working with AI tools and staying ahead of emerging trends.
This position is open to all candidates.
