Member of Technical Staff, Data Engineering

• Build our data platform from zero: Architect and implement our internal data warehouse, ingestion infrastructure, ETL/ELT pipelines, and core data models.
• Create agent-ready data foundations: Design reliable, well-documented datasets and interfaces that AI agents can safely discover, query, and use.
• Turn messy data into trusted systems: Transform fragmented operational and product data into consistent, high-quality sources of truth.
• Enable self-service analytics: Build the infrastructure that allows engineering, product, and business teams to answer questions independently without relying on manual data requests.
• Own product analytics infrastructure: Establish the event tracking, instrumentation, data models, and reporting foundations needed to understand product usage and performance.
• Automate data operations: Leverage AI coding agents and intelligent automation to accelerate pipeline development, data validation, documentation, monitoring, and troubleshooting.
• Build for reliability and scale: Establish data quality standards, observability, governance, and scalable architectural patterns from the beginning.
• Raise the engineering bar: Mentor engineers, establish strong data engineering practices, and lead architectural decisions through hands-on execution.

• Staff-level engineering expertise with extensive hands-on experience building and operating production data platforms.
• Proven greenfield experience: You've personally led the design and implementation of a data platform from scratch, rather than exclusively maintaining or extending existing infrastructure.
• Deep understanding of data warehouses, ETL/ELT architectures, data modeling, orchestration, and distributed data processing.
• Strong proficiency in SQL and Python, with the software engineering fundamentals needed to build reliable data infrastructure.
• Experience with modern data technologies such as Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, Dagster, or equivalent tools.
• Strong understanding of data quality, lineage, observability, access controls, and production data reliability.
• Experience designing analytics infrastructure that supports product insights and operational decision-making.
• AI-native engineering habits: You already use AI coding agents as part of your daily workflow and actively automate repetitive development and data engineering tasks.
• Ability to make pragmatic architectural decisions, balancing scalability, simplicity, performance, and engineering velocity.
• Demonstrated technical leadership, including mentoring engineers and establishing engineering standards.

• You've been the founding or early Data Engineer responsible for building a company's first real data platform.
• You've designed data systems that support both traditional analytics and AI-powered applications or autonomous agents.
• You have experience building semantic layers, data discovery systems, or structured interfaces that make enterprise data accessible to AI.
• You've automated substantial portions of data pipeline development, validation, or maintenance using AI agents.
• You know how to avoid overengineering while building foundations that won't need to be replaced six months later.
• You're comfortable moving between data architecture, production pipelines, analytical modeling, and hands-on debugging.

Project and contracting process: the hiring partner. Applications continue on the provider's website.