Data Eng - Analytics and GTM job opportunity at MLabs.

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Date2026-02-12T14:05:22.796Z bot
MLabs Data Eng - Analytics and GTM
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loacation New York, United States Of America
loacation New York....United States Of America

Data Eng - Analytics and GTM Location:  New york Onsite | Full-time Compensation: $140K - $220K We are hiring on behalf of our client, an aggressively growing, well-funded fintech startup (Series A, $27.9M+ raised) on a mission to bring full transparency to global trading. They are replacing the opaque, offshore brokerage model with a transparent, permissionless trading stack built on-chain. By leveraging open, auditable code, we ensure that every trade, deposit, and withdrawal is verifiable. Backed by world-class investors including General Catalyst, Jump, and Susquehanna, they are looking for a Data Engineer to build the analytics engine that will power their next phase of growth. The Role As the Data Engineer for Internal Analytics & GTM (Go-To-Market), you will own the data systems that help them understand their users, optimize their growth motion, and drive better decision-making across the business. Your mandate is to turn raw on-chain and product data into reliable, self-serve analytics that fuel acquisition, retention, and revenue. What You’ll Do Data Pipelines: Architect and maintain robust ingestion and transformation pipelines for on-chain events, product telemetry, and third-party marketing/CRM data using AWS-native services. Analytics Infrastructure: Build and own core data models—covering user journeys, cohort analysis, attribution, and LTV—to enable self-serve reporting for Growth and Marketing teams. Dashboards & Reporting: Design and maintain high-impact dashboards (Metabase, Looker, or similar) that surface key GTM metrics like activation rates and trading behavior. User & Wallet Analytics: Develop frameworks to analyze on-chain wallet behavior (deposit patterns, churn signals) and enrich them with off-chain context. Experimentation: Build the data foundations for A/B testing and growth experiments, ensuring clean event tracking and statistical rigor. Productionization: Package pipelines into reliable services with CI/CD, automated tests, and data quality checks that the entire stakeholder team can trust. Engineering Fluency: Strong experience in Python (pandas/polars, dbt) and expert-level SQL . Cloud Infrastructure: Hands-on experience with the AWS ecosystem (S3, Athena, Glue, Redshift, Lambda, Step Functions). BI & Visualization: Proven ability to build self-serve reporting layers in tools like Metabase, Looker, Preset, or Tableau. Production Mindset: Experience turning ad-hoc analysis into robust, versioned pipelines with CI/CD and reproducible environments. Bonus Points If You Have Experience with GTM analytics (attribution modeling, funnel analysis, or marketing mix measurement). Familiarity with crypto/DeFi data and on-chain event streams (The Graph, Dune, RPC logs). Experience with orchestration and data quality tools (Airflow, Dagster, Great Expectations). Exposure to product analytics tools like Amplitude, Mixpanel, or Segment.

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