Role Description Analytics Engineers at CookUnity are data product owners who sit in a single team on a mission to minimize time spent by analysts and data scientists discovering, understanding, and preparing data before launching into their work. Each analytics engineer has ownership and autonomy over a set of entities/tables in a domain, collaborating on a single data design from first principles that the team owns together. As their products mature, they merchandise them as Tableau Connections, extracts into sheets, or any other endpoint needed to achieve their mission. Some analytics engineers have an interest in data infrastructure and integrations, others in bringing their data to life through interactive visualizations, but all have the skills to work with unruly raw data, design analytics-friendly data products (including entities and flat tables), and build them to a production standard that the whole company can rely on. Everyone in data at CookUnity comes with a high agency mindset, a passion for collaborating with others, a pattern of achievement, and a sincere interest in investing in their own development. Responsibilities - Own Core Data Products - Design, build, and maintain dbt models in Snowflake that reflect key business logic and serve as reliable sources of truth. Define clear ownership, usage expectations, and roadmaps for your data products. - Build Trustworthy, Scalable Models - Develop models that are easy to use, tested, documented, and serve a wide range of stakeholders. Measure success through usage metrics, reduced support needs, and SLA coverage. - Ensure Reliability and Observability - Monitor pipelines, track schema changes, and implement testing and alerting to ensure consistent data quality and uptime. - Drive Iteration Through Feedback - Collect and act on feedback from users. Use metrics, interviews, and usage logs to continuously improve your data products and documentation. - Advance AI Adoption in Analytics - Champion the use of agentic and AI-assisted tools in data workflows. Lead pilots, measure impact, and scale successful practices across the team. - Collaborate Cross-Functionally - Work with Engineering, Product, and Ops to align on metrics, instrumentation, experimentation, and data needs. - Mentor and Raise the Bar - Review code, guide design decisions, and share knowledge to elevate the craft of Analytics Engineering within the team. Qualifications - 4+ years of experience in Analytics Engineering, Data Engineering, or a similar role working with modern data stacks (dbt, Fivetran/Airbyte, Airflow, Snowflake/BigQuery, etc.) - Deep understanding of data modeling principles (e.g. dimensional modeling, ELT best practices, testing strategies, etc.) - Proficiency in SQL and version control (Git), with experience shipping production-quality data pipelines - Experience collaborating closely with cross-functional stakeholders (Product, Engineering, Operations, etc.) to translate business needs into data solutions - Comfort navigating ambiguity in fast-paced environments, with a strong sense of ownership and problem-solving mindset - Familiarity with BI tools (e.g. Tableau, Hex, Looker) and a passion for enabling self-serve data culture - Excellent communication skills, with the ability to explain technical concepts to non-technical audiences Benefits - 💸 Get paid in USD - 🗺 Work remotely: design the life that you want - ⛱ Enjoy 15 days of vacation each year from the start date - 🎄 16 fully paid Argentinean holidays - 🩺 Healthcare Benefit: Monthly stipend to use in your preferred healthcare provider - 🗓️ 5-year Sabbatical: After 5 years with CookUnity, you get a 4-week paid sabbatical - 🐣 Paid Family leave - 🕯 Compassionate Leave: 3-5 days each time the need arises - 🧘🏽‍♀️ Customize the benefits that suit your needs! Access a range of perks tailored to you, including learning opportunities, wellness memberships, delivery apps, and more through our comprehensive benefit platform - 🧑‍🏫 Personalized English coach

Role Description At Oversee, we are reimagining how corporate travel is managed. Our advanced technology and machine learning eliminate outdated, manual processes that hold companies back - driving cost savings, improving efficiency, and enabling smarter decision-making. We combine travel analytics with actionable insights, giving global enterprises the tools to gain visibility into their travel spend while continuously optimizing their programs. In this role, you’ll have meaningful influence over data architecture, tooling, and how AI is embedded into internal workflows and product development. It’s a strong fit for someone who enjoys solving complex data problems, working closely with product and engineering, and building systems that directly impact the business. What you’ll do: - Own the data architecture for one of our products, designing, building, and continuously evolving ETL/ELT pipelines as the product and its data model change rapidly. - Support customer-facing work: data requests, POCs, and analyses that directly influence commercial outcomes. - Drive data quality and architecture decisions independently; this role requires judgment, not just execution. - Develop HTML-based dashboards and visualizations that surface the metrics and funnel visibility the product and business teams need to act. - Build and maintain data endpoints consumed by React-based product pages, stepping into front-end development when needed. - Bring genuine AI fluency to your workflow; we’ll ask you how you’d use AI to improve what you’re building, and we expect a real answer. Qualifications - 5+ years in analytics engineering, data engineering, or a closely related role - with strong, proven data modeling and ETL/ELT experience. - Python is a must; you use it regularly and confidently for transformation, automation, or analysis. - Strong SQL and a solid grasp of data warehouse architecture; hands-on experience with Snowflake or dbt is a significant plus. - Senior-level independence; you can own ambiguous problems, make architecture calls, and communicate tradeoffs clearly to non-technical stakeholders. - An active and thoughtful AI user; you have a real point of view on how AI changes data work, and you back it up with how you actually operate day-to-day. - Comfortable contributing to front-end layers or working directly alongside React engineers without friction. - Strong analytical mindset - business-oriented, funnel-aware, and able to go from raw data to insight without being handed the question first. - BS in Computer Science, Statistics, Industrial Engineering, or a related quantitative field preferred. Requirements - Data warehouse: Snowflake, Dbt - Analytics & Visualization: Power BI - Languages: SQL, Python - Data orchestrator: Dagster Benefits - Dynamic, fast-paced, and positive environment. - Opportunity to solve a $17B global problem. - Exponential YoY revenue growth. - Partnerships with some of the world’s largest Fortune 500 companies. Company Description We are proud to be an equal opportunity workplace. We provide employment opportunities without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, or veteran status.

• Architect, Design and implement ETL processes. • Develop and maintain integration tools and utilities. • Proficient in writing SQL queries and troubleshooting issues. • Support existing applications. • Provide guidance and support to Junior/Mid-level developers. • Instruct, direct, and check the work of others. • Lead, design, code, test and debug complex development efforts. • Document, enforce and model best standards and practices to others. • Oversee project completions and ensure user satisfaction. • Complies with Company policies, procedures, regulatory requirements, and complete mandatory company compliance training by prescribed deadlines. • Ability to accept guidance and supervision from management. • Other duties that may be reasonably assigned.

• Revenue Data Layer Ownership: Design, build, and maintain the foundational revenue data models in dbt – ARR, pipeline, bookings, churn, expansion, retention, attach rates, and the data models that join them to customer, opportunity, and product context. This is your domain end-to-end. • Stakeholder Partnership: Establish strong, durable working relationships with leaders and operators across Sales, Customer Success, and Revenue Operations. Translate loose, evolving requirements into well-specified models and clear metric definitions that stakeholders trust and can actually use. • Requirements Gathering: Sit with the people who use the data. Understand the operational reality behind a request before writing any SQL. Distinguish the actual decision being made from the artefact that was asked for, and bring back a sharper proposal than the one you were handed. • Pragmatic Delivery: Be excellent at 80/20-ing. Ship the version that unlocks the decision this week, while building the foundations that hold up next quarter. Know when a quick dbt model and a clear caveat is the right call, and when the situation calls for a proper, governed, tier-one data product. Make that judgement explicit, not implicit. • Modeling Craft: Author dbt models that are well-tested, well-documented, performant, and legible to the next engineer who picks them up. Establish and uphold conventions for naming, layering (staging → intermediate → marts), testing, and documentation across the revenue domain. • Data Products & Tiering: Build to the right tier for the use case – from exploratory sandbox models for analyst-driven discovery, through self-serve modeled marts, up to the regulated, accuracy-critical models that feed Finance, board reporting, and the executive team. Be opinionated about what belongs where. • Commercial Acumen: Bring a strong intuition for how B2B SaaS revenue actually works – the relationships between bookings, billings, ARR, recognised revenue, pipeline coverage, conversion, sales cycle, and retention – and let that intuition shape the models you build, not just validate them after the fact. • Attention to Detail: Revenue numbers get scrutinised. Reconcile to source-of-truth systems, document edge cases, surface assumptions, and make sure the number in the board deck is the same number in a dashboard. Sweat the small stuff because the executive team will. • Partner with Data Engineering: Work closely with the data engineering team on what lands in the warehouse from CRM, billing, product, and finance systems. Be a clear, specific, respectful customer of sourcing – the kind of partner that makes the upstream team’s job easier, not harder.