This is a hands-on building role: you turn raw, messy fabrication data into the clean, well-modeled, AI-ready datasets that our AI/ML and analytics workloads run on

‍ Responsibilities:
Build and operate ingestion, ELT/ETL, and orchestration pipelines that move data from our MongoDB Atlas operational store and other sources into our analytical and AI-serving layers

Implement layered (medallion-style) transformations with idempotent, backfillable, incrementally loaded jobs

Apply deduplication, normalization, and validation so downstream data is high-quality and trustworthy

Modernize legacy / homegrown data flows via incremental, strangler-fig migrations that keep production stable

Build embeddings and vector pipelines, and the feature/retrieval-ready datasets that RAG, semantic search, and agentic workloads depend on

Make production data AI-ready in practice: well-structured, lineage-tracked, and retrieval-friendly, in partnership with ML and application engineering

Implement real-time and change-data-capture flows from MongoDB (Change Streams / CDC) where workloads require fresh data

Implement the canonical data model, schemas, and data contracts defined by the Data Architect — enforced in-repo so other teams build against stable definitions

Exercise sound persistence judgment in execution: land data in the right store (document / NoSQL, vector, analytical) per the architectural direction

Contribute to build-vs-buy decisions by prototyping with proven, industry-standard tooling over custom development

Establish testing, data-quality, and lineage checks for the pipelines you own, with clear alerting and runbooks

Instrument pipeline observability (freshness, volume, schema-drift, cost)



so failures are caught before consumers feel them

Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier for transformation logic, query tuning, and migration scripting

Partner with database engineering on extracting from and protecting the production store

Partner with the Data Architect on implementing target-state patterns and surfacing what's hard to build

Partner with ML, AI, and application engineers on the data they consume — shaping and governing it so it's safe and ready to build on

If you have:
5+ years of hands-on data engineering experience building and operating production data pipelines at scale

Strong programming and data skills: Python and SQL, with solid software-engineering fundamentals (version control, testing, CI) — shipping and maintaining production code, not just notebooks

Hands-on MongoDB at production scale (Atlas adecuado): document modeling, aggregation framework, change streams / CDC, and extracting from a document store into analytical / AI-serving layers. Our stack is NoSQL / MongoDB, not relationa l , this is a core requirement, not an extra

Demonstrated experience with ELT/ETL pipeline design, transformation frameworks (dbt or equivalent), 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

Hands-on experience 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 production (MongoDB Atlas Vector Search or equivalent)

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

Strong grasp of data quality, testing, lineage, and pipeline observability practices

Comfortable working in a complex, specialized domain. MEP / AEC / construction experience is a plus; appetite to learn the domain is required

It’s a plus:
Experience with the Azure data ecosystem (Data Factory, Synapse Analytics, Azure Functions, Event Grid)

Lakehouse platforms (Databricks, Snowflake) or open table formats (Iceberg, Delta, Hudi); feature stores (Feast or equivalent)

Streaming / event-driven data processing (Kafka, Event Hubs, Spark Structured Streaming)

CDC and cross-engine sync (MongoDB Change Streams, Debezium, or equivalent)

Experience with geometric / BIM / CAD data or other multi-modal, unstructured source data

Knowledge-graph, ontology, or semantic-layer exposure

Data governance for AI/agent access to production data: query-cost controls, read-path safety, lineage, audit

SOC 2 and data-classification awareness

This call is made within the framework of Law 19.691 on the Promotion of Employment for Persons with Disabilities, including individuals registered in the National Registry of Persons with Disabilities of the Ministry of Social Development

📌 Senior Data Engineer (Ciudad de México)
🏢 Marvik
📍 Ciudad de México