Make an impact
of your own.

The company

nocnoc is the leading e-commerce facilitator for global brands and retailers looking to increase their sales in Latin America.

We enable sellers around the world to easily access +15 marketplaces through one single platform offering their products to over 500 million online customers. We are committed to connecting Latin America with the world through e-commerce.

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 Sr. Analytics Engineer, 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. Your work will enable Business and BI teams to consume certified, trustworthy and well-documented data, reducing operational friction and allowing the Data team to focus on building scalable data architecture.

You will be in charge of executing the following responsibilities:

• Define the main data domains in alignment with business and BI needs.
• Design and build the Gold Layer in S3, creating business-ready tables optimized for BI and self-service consumption.
• Design intuitive data models that reduce dependency on the Data team, with a target of 40% reduction in ad-hoc data requests.
• Create a clear documentation blueprint, including:
• Entity-relationship diagrams
• Complete and accurate metadata in OpenMetadata
• Make data models available in BI tools, configuring data connections and defining semantic layer elements.
• Partner with BI and business teams to ensure the Gold Layer fully supports analytical and decision-making needs.

What experience will help you in this role?

• Proven ability to translate business requirements into optimal, scalable data models.
• Strong experience in data transformation with an engineering mindset.
• Advanced SQL, Python skills and experience modeling analytical data.
• Experience working with data lakes / lakehouse architectures, ideally on S3.
• Familiarity with PySpark and AWS Glue
• Familiarity with migrating or decoupling workloads from traditional data warehouses (e.g. Redshift).
• Experience operating or contributing to data governance and metadata tools.
• Exposure to BI tools (Tableau, QuickSight, Metabase) and understanding their modeling requirements is a plus.

What do we value the most?

• Ownership, results-driven mindset
• Feel comfortable with dynamic changes as well as high speed growth
• Team player
• Empathy

Thank you for reading and we hope to meet soon!