Analytics Engineer at Clay Labs

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

26 Apr, 26

Salary

0.0

Posted On

26 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Analytics Engineering, Data Engineering, Data Management, SQL, Python, AWS, dbt, Dagster, Airflow, Data Quality, Data Validation, Data Governance, Collaboration, ETL, Data Pipelines, Data Models

Industry

Software Development

Description

About Clay Our mission is to help organizations turn any growth idea into reality. We see growth as a creative practice, not a formula. Finding and reaching your best-fit customers takes unique ideas and constant iteration. As AI makes execution faster and tactics easier to copy, creativity is the only lasting advantage. We're already helping thousands of customers — including Anthropic, Waste Management, Figma, and Ramp — go to market with unique data, signals, and AI research. In 2025, we crossed $100M in revenue and raised a $100M Series C at a $3.1B valuation, backed by world-class investors including Sequoia, CapitalG, and First Round. We also completed our first first employee tender offer and launched a community equity round, for our customers, agency partners, and club members. Some things to know about us: Our community includes 11,000+ customers, 150+ integration partners, 125+ agencies, 50+ Clay clubs, and 30k members on Slack. Our culture is unique inside and outside of work. Our team members are also DJs, activists, writers, clowns, marathoners, skydivers, psychedelic therapists, social workers, and more. All employees can work for free with world-class coaches who specialize in creativity, management, and more. Our operating principles — including negative maintenance and non-attached action — guide our work. Read more about them here. Read about us in the NYT, Forbes, First Round Review, and more. Hear from our employees directly on our Glassdoor page! Analytics Engineering @ Clay We’re looking for an Analytics Engineer to bridge the gap between raw data infrastructure and high-impact business insights and data products. You’ll take ownership of our data models, pipelines, and platform architecture to ensure our analytics and data science foundation is scalable, durable, and consumable. Your work will enable better decision-making across teams—from product and engineering to marketing, finance, and ops. Clay’s modern data stack includes Fivetran, Segment, Snowflake, dbt, Dagster, Streamlit, Hex, Sigma, Eppo, Census, Cube.dev —and you'll play a key role in shaping how we use and evolve it to power strategic insight and operational excellence. What you'll do Own data pipelines: Design, build, and maintain reliable ETL/ELT pipelines, ensuring timely and accurate delivery of high-quality data across the organization. Build a scalable foundation: Find ways to leverage and improve our current data models by architecting resilient, maintainable dbt models that serve as the trusted source of truth for analytics, experimentation, and data products. Collaborate cross-functionally: Work closely with data scientists, engineers, and business teams to understand use cases and proactively develop data assets that support their goals effectively and scalably. Drive architectural improvements: Identify opportunities to consolidate and improve our data platform, proposing improvements that ensure long-term scalability and flexibility. Improve data quality, discoverability, and metric clarity: Design and implement robust systems for schema design, data validation, documentation, and governance, while defining and standardizing core business metrics and semantic definitions to ensure consistency across teams and tools. Support self-serve insights: Enable teams with intuitive, trustworthy data products and tooling that allow less-technical users to explore data and develop solutions independently. Plan for the future: Help define the evolution of Clay’s data stack and architecture, including how we consolidate, transform, and surface data and intelligence for emerging use cases. What you'll bring 6+ years of hands-on experience in analytics engineering, data engineering, or data management Strong ownership mentality and proven ability to design for scalability, durability, and reusability Expert in SQL, Python, AWS infrastructure, and modern data tools (emphasis on dbt, Dagster/Airflow) A passion for pragmatic solutioning while staying current on modern data and AI tooling, pushing the boundaries of GenerativeAI’s role in Data Products and workflows Excitement for iterative, agile development processes and collaboration with diverse teams

Responsibilities

The Analytics Engineer will own data pipelines, designing and maintaining reliable ETL/ELT processes to ensure timely and accurate data delivery. They will also collaborate cross-functionally to develop data assets that support various business goals effectively.