# AI Data Engineer [Katapult Labs](https://gurify.com/jobs?q=Katapult%20Labs) · Colombia · Posted yesterday Full-time [Data](https://gurify.com/jobs/data) [AI & ML](https://gurify.com/jobs/ai-ml) [Apply on the original posting → (opens in a new tab)](https://jobs.ashbyhq.com/katapult-labs/c6f92b44-a4d5-4554-806e-857621d82486?utm_data=hiring.lat) ## Job description ### AI DATA ENGINEER ### Katapult Labs · Remote (LATAM) · Full-time ### ABOUT KATAPULT Katapult is an AI-first engineering studio connecting senior LATAM talent with startups and companies in the US. We're not a staff-augmentation shop: every person we put in front of a client represents Katapult, co-creates product, and owns the outcome directly, with no managers or leads acting as a layer in between. ### THE OPPORTUNITY We're looking for a Data Engineer who builds, not just maintains. You'll work with one of our US startup partners to create the data foundation behind their product and business decisions. That means bringing scattered sources into one trusted place, keeping the data flowing reliably, and turning it into insights the team can act on. This isn't a back-office pipelines role. You'll work side by side with product and business leaders. You'll need to understand what the company is trying to achieve and why each dataset matters. Then you'll use that context to decide what to build next. ### WHAT YOU'LL DO - Design, build, and own ETL/ELT pipelines that bring data from multiple sources (APIs, SaaS tools, CRMs, transactional databases) into Snowflake. - Model data in Snowflake so it's clean, trustworthy, and easy to use for analytics and product. - Build and maintain integrations across the stack. You'll decide when to use off-the-shelf connectors and when to write custom code. - Work directly with product and business leaders to turn vague questions into concrete data solutions. - Use AI tools and agents to speed up your own work and to automate manual workflows for the team. - Take ownership end to end: spot the problem, propose the solution, ship it, and keep improving it. ### WHAT WE'RE LOOKING FOR - Hands-on data engineering experience. You've built production pipelines yourself, in real code (Python and SQL), not only through dashboards or notebooks. - Strong Snowflake experience, including data modeling, performance, and cost awareness. - Experience integrating multiple data sources and orchestrating pipelines (Airflow, dbt, Fivetran/Airbyte, or similar). - Agentic . Experience building AI agents or LLM-powered workflows on top of company data. - Business and product sense. You can talk with non-technical stakeholders, understand the business model, and prioritize by impact. - Impeccable English, spoken and written. You'll work directly with a US team daily. - Comfortable in an early-stage, ambiguous environment where you're a builder, not a ticket-taker. ### NICE TO HAVE - Background in startups or a founder/co-founder experience. - Familiarity with CI/CD, testing, and version control practices for data (Git, dbt tests, data quality checks). - Cloud experience (AWS, GCP, or Azure). **Live in Katapult Labs’s hiring system.** Read from the company's own applicant tracking system, not reposted from a job board — so it's a real, open requisition rather than an ad that outlived the role. 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We\u0026#39;re not a staff-augmentation shop: every person we put in front of a client represents Katapult, co-creates product, and owns the outcome directly, with no managers or leads acting as a layer in between.\u003C/p\u003E\u003Ch3\u003ETHE OPPORTUNITY\u003C/h3\u003E\u003Cp\u003EWe\u0026#39;re looking for a Data Engineer who builds, not just maintains. You\u0026#39;ll work with one of our US startup partners to create the data foundation behind their product and business decisions. That means bringing scattered sources into one trusted place, keeping the data flowing reliably, and turning it into insights the team can act on.\u003C/p\u003E\u003Cp\u003EThis isn\u0026#39;t a back-office pipelines role. You\u0026#39;ll work side by side with product and business leaders. You\u0026#39;ll need to understand what the company is trying to achieve and why each dataset matters. Then you\u0026#39;ll use that context to decide what to build next.\u003C/p\u003E\u003Ch3\u003EWHAT YOU\u0026#39;LL DO\u003C/h3\u003E\u003Cul\u003E\u003Cli\u003EDesign, build, and own ETL/ELT pipelines that bring data from multiple sources (APIs, SaaS tools, CRMs, transactional databases) into Snowflake.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EModel data in Snowflake so it\u0026#39;s clean, trustworthy, and easy to use for analytics and product.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBuild and maintain integrations across the stack. 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You\u0026#39;ve built production pipelines yourself, in real code (Python and SQL), not only through dashboards or notebooks.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EStrong Snowflake experience, including data modeling, performance, and cost awareness.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EExperience integrating multiple data sources and orchestrating pipelines (Airflow, dbt, Fivetran/Airbyte, or similar).\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EAgentic . Experience building AI agents or LLM-powered workflows on top of company data.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EBusiness and product sense. You can talk with non-technical stakeholders, understand the business model, and prioritize by impact.\u003C/li\u003E\u003C/ul\u003E\u003Cul\u003E\u003Cli\u003EImpeccable English, spoken and written. 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