Sr. Data Engineer

About Olé Life Olé Life is a digital life insurance company for Latin America, with growing operations in Mexico and Brazil and international reinsurance partners. We're building a small, senior team where every person has real ownership and AI is part of the daily workflow. The role This isn't a maintenance position: you'll be the owner of the company's entire data platform , end to end. Today the data function is spread thin with accumulated operational debt; your mandate is to consolidate it, automate what's manual, clean up governance, and turn data into decisions for Revenue, Operations, Underwriting, and our international teams. You'll coordinate medium-to-large projects directly with business stakeholders, with a lot of autonomy and leadership backing. What you'll do Own the warehouse and pipelines (GCP): administer and evolve BigQuery (bronze/silver/gold architecture), the ingestions from the policy core via Cloud Functions, and move pipelines to an incremental, managed setup (Dataform or similar), optimizing models, joins, and query costs. Automate recurring reporting: turn reports that are manual today — like the quarterly reinsurer report, which takes weeks of manual work — into reliable pipelines with clear ownership, and sustain critical dashboards like daily Revenue. Semantic layer and BI: consolidate the semantic model (Cube/Metabase, with a pending migration to Cloud Run), manage the BI stack (Power BI, with Looker evaluation underway), and establish single, governed metric definitions across the company. Data for Underwriting — the area of highest impact: complete the tracking of application form responses, build the prescreening data pipeline, automate follow-ups and notifications that are manual today, and produce the analysis needed to raise the automatic approval rate (ASA) from the current ~20% toward the 70% potential that's been identified. Data governance and security: classify sensitive data with Dataplex (Public / Internal / Confidential / PII levels), manage access and roles in GCP, separate dev/prod environments, and support regulatory initiatives (Brazil's LGPD). Operational data systems: support the incentives system and automated sends to agencies and agents (FlowFunction), provide data support to Operations for prescreening, and support international OKRs (Mexico and Brazil). Demand management: be the entry point for the company's data requests — triage, prioritization, and status communication — replacing the current ad-hoc Slack/email flow with a process that scales. Documentation and continuity: document pipelines, definitions, and runbooks so knowledge lives in the system, not in people. What we're looking for 5+ years of experience in data, at least 3 of them as a Data Engineer, with a track record coordinating medium-to-large projects end to end (discovery → build → delivery → adoption) with non-technical stakeholders. Expert SQL and solid experience with BigQuery / GCP (or an equivalent warehouse, with a willingness to ramp up on GCP quickly). Python for pipelines and automation; experience with transformation/orchestration tools (Dataform, dbt, Airflow, or similar). Experience with BI and semantic modeling (Power BI, Metabase, Looker, or similar). Real autonomy: able to operate with ~80% of the context and go build the remaining 20% without being asked. Excellent time management: you'll balance daily operational demand with longer-term projects, which takes strong prioritization and even stronger communication. Use of AI tools (Claude, Copilot, or similar) as a natural part of your daily workflow. Fluent Spanish; professional/technical English (we work with international partners). Nice to have Experience in insurance, fintech, or other regulated industries. Experience with data governance and security (IAM, classification, handling PII). Having been the first or only data person at a company, or part of small, highly autonomous teams. Familiarity with Supabase/Postgres, product analytics (Amplitude), or notification-flow automation. How we work A small, senior team: few people, a lot of ownership, zero micromanagement. AI is part of daily work, not an experiment. Direct, visible impact: your pipelines feed real business decisions, from daily revenue to policy approvals.