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The Opportunity
At nocnoc, data is critical for decision-making, but today there is a strong dependency on the Data team to answer ad-hoc business questions due to disorganized data, limited documentation, and fragmented models across Redshift and S3. This role is key to breaking that dependency loop. As an Analytics Engineering Lead, your mission will be to transform the Data team from a "manual answers center" into a self‑service data platform provider, by designing and building the Gold Layer of our new Lakehouse architecture. Our focus will be on business logic, data modeling, and SQL craftsmanship — translating how the business actually works into clean, intuitive, well‑documented data models that Business and Tech teams can confidently self‑serve from. Responsibilities
Define the main data domains in alignment with business and tech needs. Design and build the Gold Layer, creating business‑ready tables optimized for self‑service consumption on top of the Silver Layer delivered by Data Engineering. Design intuitive data models (dimensional modeling, star/snowflake schemas, wide analytical tables) that reduce dependency on the Data team, with a target of 40% reduction in ad‑hoc data requests. Write production‑grade SQL transformations that encode business logic clearly, consistently, and in a maintainable way. Entity‑relationship diagrams. Complete and accurate metadata in Open Metadata. Definitions of business metrics, dimensions, and grain. Make data models available in BI tools, configuring data connections and defining semantic layer elements.



Partner with business teams to ensure the Gold Layer fully supports analytical and decision‑making needs. Collaborate with Data Engineering to define requirements and contracts for the Silver Layer that feeds your models. Requirements
Proven ability to translate business requirements into optimal, scalable data models. Advanced SQL skills, including complex transformations, window functions, performance‑aware query design, and modular/reusable logic. Strong experience in analytical data modeling (dimensional modeling, facts and dimensions, slowly changing dimensions, handling grain and historization). Experience working with data lakes/lakehouse architectures, ideally on S3. Solid understanding of how business processes translate into data — comfortable interviewing stakeholders, mapping entities and metrics, and challenging assumptions. Experience making data models consumable in BI tools (Tableau, Quick Sight, Metabase, Looker, or similar) and shaping a semantic layer. Experience leading teams and projects, and partnering across business and tech. Fluent Spanish (native). Basic English level (written and reading). Nice to Have
Working knowledge of Python for data transformation tasks. Exposure to PySpark, AWS Glue, or dbt — useful as context, but pipeline development and orchestration are owned by the Data Engineering team. Experience with data governance and metadata tools (Open Metadata, Data Hub, Atlan, etc.). Familiarity with migrations or decoupling workloads away from traditional data warehouses such as Redshift. What We Value the Most
Feel comfortable with dynamic changes as well as high‑speed growth. Team player. Empathy.
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📌 Analytics Engineering Lead (Florencio Varela) (Argentina)
🏢 nocnoc
📍 Argentina