Remote Data Engineer (Latin America)

About the role

Azumo builds and operates production AI systems for clients from seed-stage startups to Meta, and is hiring a Data Engineer to own ingestion and transformation pipelines, storage and warehouse design, and the retrieval infrastructure that AI systems query. The role is fully remote across Latin America, aligned to the client's working day.

Key responsibilities

• Ingestion and transformation pipelines: batch and streaming on Spark, Kafka, dbt and Airflow, with idempotency, backfills, schema evolution and late-arriving data.
• Storage and modeling: warehouse and lakehouse design on Snowflake, BigQuery, Redshift or Databricks, with partitioning, file layout and query cost as engineering decisions.
• The retrieval layer: chunking, embedding and indexing pipelines feeding RAG systems on pgvector, Pinecone, Qdrant or Azure AI Search, plus freshness, deduplication and permission issues.
• Data quality as a contract: tests, expectations, lineage and alerting so downstream users know when a pipeline is wrong.
• Sensitive data by default: PII classification, masking, row and column-level access, retention, deletion and an audit trail.
• Production operation: containerized deployment on Azure or AWS, CI/CD, orchestration, observability and cost and runtime budgets.
• Working inside the client's environment, with client repositories and standups; some engagements carry additional requirements such as HIPAA.

Required profile

• 5+ years building and operating production data pipelines, with Python and SQL as primary languages, plus testing, code review, CI/CD, Git, containers and orchestration.
• Deep expertise in data warehouses or lakehouses, including dimensional modeling and incremental processing.
• Distributed processing at production scale with Spark, Kafka, Flink or equivalent, and orchestration with Airflow, Dagster or Prefect.
• Transformation under version control with tests and lineage (dbt or self-built).
• Cloud deployment experience, Azure preferred and AWS acceptable, with Docker, CI/CD pipelines and infrastructure as code (GitHub Actions, Terraform or Bicep).
• Clear written and spoken English (C1 or above) and the ability to explain technical trade-offs to a client.
• Bachelor's degree in Computer Science, Data Science or a related field, or equivalent professional experience.

Preferred qualifications

• Vector and retrieval infrastructure (pgvector, Pinecone, Qdrant, FAISS, Azure AI Search).
• Streaming, real-time or high-throughput workloads.
• Managed cloud services such as Airflow, Glue, Elastic stack, Redshift, Snowflake, BigQuery, Azure SQL Db, EMR, Databricks.
• Jupyter or Google Colab notebooks, SOC 2 or HIPAA compliance experience, open-source contributions or technical writing.

What we offer

• Paid time off (PTO) and U.S. holidays.
• AI training and mentored career development.
• Profit sharing.
• Remuneration in U.S. dollars.