Senior Data Engineer

This is a fully remote position, open to applicants in Uruguay.

📋 Description

• Design and manage ingestion, ELT/ETL, and orchestration pipelines that transport data from our MongoDB Atlas operational database and other sources into our analytical and AI-serving infrastructures.

• Execute layered (medallion-style) transformations with idempotent, backfillable, and incrementally loaded jobs.

• Conduct deduplication, normalization, and validation to ensure downstream data is of high quality and reliable.

• Revamp legacy or homegrown data flows through incremental, strangler-fig migrations that maintain production stability.

• Develop embeddings and vector pipelines, along with the feature/retrieval-ready datasets essential for RAG, semantic search, and agentic workloads.

• Prepare production data to be AI-ready in practice: well-structured, lineage-tracked, and retrieval-friendly, in collaboration with ML and application engineering.

• Implement real-time and change-data-capture workflows from MongoDB (Change Streams / CDC) where current data is necessary.

• Enact the canonical data model, schemas, and data contracts as defined by the Data Architect—enforced in-repo to ensure other teams build against consistent definitions.

• Apply sound persistence judgment in execution: store data appropriately (document/NoSQL, vector, analytical) per architectural guidance.

• Assist in build-vs-buy decisions by prototyping with recognized, industry-standard tools rather than custom development.

• Establish testing, data quality, and lineage verification for the pipelines you manage, with transparent alerting and runbooks.

• Implement pipeline observability (freshness, volume, schema drift, cost) to detect failures before they impact consumers.

• Utilize AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for transformation logic, query optimization, and migration scripting.

• Collaborate with database engineering to extract from and safeguard the production store.

• Work alongside the Data Architect to implement target-state patterns and address challenging builds.

• Partner with ML, AI, and application engineers on the data they utilize—shaping and governing it to ensure it is secure and prepared for development.

⛳️ Requirements

• Over 5 years of practical data engineering experience in developing and managing production data pipelines at scale.

• Proficient programming and data skills: Python and SQL, with strong software engineering fundamentals (version control, testing, CI)—shipping and maintaining production code, not just notebooks.

• Hands-on experience with MongoDB at production scale (Atlas preferred): including document modeling, aggregation framework, change streams / CDC, and extracting from a document store into analytical/AI-serving layers.

• Proven experience in ELT/ETL pipeline design, transformation frameworks (dbt or similar), and orchestration (Airflow, Dagster, or Azure Data Factory).

• Experience building on cloud-native data platforms and lake/lakehouse/warehouse architectures, with layered (medallion-style) modeling.

• Practical experience in preparing data for AI/ML or analytical consumers—embeddings/vector pipelines, RAG-/feature-ready datasets, or equivalent—including deduplication, normalization, and validation.

• Familiarity with vector search and embeddings in a production environment (MongoDB Atlas Vector Search or similar).

• Demonstrated use of AI-assisted development tools (Claude Code, Copilot, Cursor) for data and pipeline tasks.

• Strong understanding of data quality, testing, lineage, and pipeline observability practices.

• Comfortable working in a complex, specialized domain; MEP/AEC/construction experience is a plus, but a willingness to learn the domain is essential.

🏝️ Benefits

• Competitive salary

• Flexible working hours

• Professional development budget

• Home office setup allowance

• Global team events