Senior Data Engineer

Svitla Systems Inc. is looking for a Senior Data Engineer for a full-time position (40 hours per week) in Argentina, Costa Rica, Colombia, Mexico and Chile. Our client is a developer of a planning platform that links prospective community college students with universities.

We're looking for a Senior Data Engineer to join our growing product and engineering team. This is a hands-on role where you'll design, build, and scale features across our platform while serving as the data engineering subject matter expert for the team. You'll own critical product features end-to-end, build data-driven experiences that surface insights to university stakeholders, and guide architectural decisions on data ingestion and modeling. You'll be equally comfortable shipping production TypeScript code, designing scalable ETL pipelines, and collaborating with business teams to translate requirements into trusted analytics - all in a high-velocity, mission-driven environment transforming how higher education serves transfer students.

Requirements

• 5-8 years as a data engineer, currently hands-on and writing production code regularly. This is an individual contributor role, not an architecture or database administration role.
• Has personally owned a data warehouse with a real transform layer (BigQuery or Snowflake with Dataform or dbt)
• Hands-on experience with model layers: bronze, silver, and gold or staging, intermediate, and marts.
• Has modeled data whose identity or attributes change over time, and can describe a specific case: which entity, which attribute changed, and why history had to be preserved rather than overwritten.
• Deep expertise with relational and NoSQL databases (PostgreSQL, MongoDB), including query optimization, schema design, indexing strategy, and practical MongoDB patterns such as soft deletes and uniqueness constraints.
• Hands-on experience with BigQuery (or comparable cloud data warehouse), a transformation framework such as Dataform or dbt, and LookML-based Looker development.
• Proven ability to translate business requirements into technical solutions, including building in-product analytics experiences and dashboards that empower non-technical stakeholders.
• Self-starter with a data-first mindset. Thrives in fast-moving, high-growth environments and can effectively guide teams on data architecture through collaboration and clear technical leadership.
• Strong written and verbal communication skills; visible and effective partner across engineering, product, and business stakeholders in async and synchronous forums.

Responsibilities

• Architect and implement scalable APIs, real-time data processing pipelines, and AI-driven features that power student-university interactions on the EdVisorly platform.
• Build in-product analytics experiences that expose insights and reporting to university stakeholders, enabling data-driven enrollment decisions.
• Design and maintain ETL pipelines and data transformation workflows (BigQuery + Dataform/dbt) that power analytics, reconciling operational stores with the data warehouse.
• Build ingestion from production operational stores (PostgreSQL, MongoDB) into the warehouse, including handling schema drift, deletions, and malformed records.
• Design and maintain historical and slowly changing data models, versioning, effective dates, point-in-time history, and canonical IDs for entities that get renamed or merged, so that reporting stays accurate as records change over time.
• Create and maintain Looker dashboards in collaboration with business stakeholders. Build aggregated and denormalized serving layers for both dashboards and AI agents.
• Implement data validation, quality checks, change management, and auditing/traceability practices to ensure data integrity across all systems.
• Maintain the semantic context, table and column descriptions, Dataform tags, and golden queries that ground AI/agent responses in trusted definitions.
• Serve as the data engineering SME for the team: review data ingestion patterns from customer systems, optimize schemas and indexing strategies (including MongoDB partial unique indexes), and guide architectural decisions.
• Partner with Solutions Engineering and Customer Success to design data integrations and reporting for enterprise customers.