Data Engineer

Azumo; Colombia; Remote; COP 142,392,000 - 284,783,000; Full time

Benefits offered by this job

Paid time off (PTO)

U.S. Holidays

AI Training

Mentored career development

Profit sharing

$US remuneration

Job summary

Azumo is hiring a Data Engineer to own ingestion, transformation pipelines, storage design, and the retrieval infrastructure that AI systems query. The role is fully remote across Latin America, aligned to the client's working day and production data systems since 2016.

You will build batch and streaming pipelines using Spark, Kafka, dbt, and Airflow, design warehouse and lakehouse models (Snowflake/BigQuery/Redshift/Databricks), and implement robust data quality, governance, and cost controls.

Qualifications

• 5+ years building and operating production data pipelines with Python and SQL
• Experience designing data warehouses or lakehouses and cost/performance trade-offs
• Experience with distributed processing at production scale (Spark, Kafka, Flink)
• Orchestration with Airflow, Dagster or Prefect including retries and backfills
• Transformation under version control with dbt or equivalent
• Cloud deployment experience (Azure/AWS) with Docker and CI/CD

Responsibilities

• Own ingestion and transformation pipelines end-to-end
• Design and maintain storage and warehouse models
• Develop retrieval layer for RAG systems and embeddings
• Ensure data quality, lineage, and alerting
• Manage security, access controls and audit trails
• Deploy and monitor production pipelines in cloud environments
• Collaborate with clients and internal teams in LATAM time zones

Skills

Python

SQL

Data pipelines

Distributed processing

English proficiency

Education

Bachelor's degree in CS/DS or related field

Tools

Spark

Kafka

dbt

Airflow

Snowflake/BigQuery/Redshift/Databricks

Job description

Azumo builds and operates production AI systems for companies ranging from seed-stage startups to Meta. We are hiring a Data Engineer to own the layer everything else depends on: 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 your client's working day.

You will not be building pipelines that work until the schema changes. Azumo has shipped production data systems since 2016, and the work here is judged downstream: whether a model can be trained on what you deliver, whether a retrieval query returns the right passage, whether a number survives being questioned by the client.

Where this role sits

Azumo's engineering organization is built around four lanes. The Data Scientist lane owns the question and the method. The AI Engineer lane owns production behavior. The Software Engineer lane owns AI-augmented product delivery. This role is the Data Engineer lane, and it owns pipelines, storage, and the retrieval layer.

One question places the boundary: when the output is wrong, whose problem is it? "The data was missing, stale, or wrong by the time it arrived" is yours. "The system did the wrong thing with data that was correct" is the AI Engineer's.

Not quite your profile? Check our other openings:

• If you decide what to measure and which method answers it — Data Scientist
• If you own how an AI system behaves in production — AI Engineer
• If you ship product software with agents in your toolchain — AI-Augmented Software Engineer
• If you've done all of the above and answered to the client directly — Forward Deployed Engineer

What You Will Build

• Ingestion and transformation pipelines. Batch and streaming ingestion on Spark, Kafka, dbt and Airflow, with idempotency, backfills, schema evolution and late-arriving data handled by design rather than by hand
• Storage and modeling. Warehouse and lakehouse design on Snowflake, BigQuery, Redshift or Databricks, with partitioning, file layout and query cost treated as engineering decisions
• The retrieval layer. The chunking, embedding and indexing pipelines that feed RAG systems on pgvector, Pinecone, Qdrant or Azure AI Search, and the freshness, deduplication and permission problems that come with them
• Data quality as a contract. Tests, expectations, lineage and alerting. If a pipeline is wrong, the people downstream should hear it from you and not from the client
• Sensitive data by default. PII classification, masking, row and column level access, retention and deletion, and an audit trail that holds up when a client asks who read what
• Production operation. Containerized deployment on Azure or AWS, CI/CD, orchestration, observability, and explicit cost and runtime budgets that you own rather than discover after the invoice
• Work inside the client's environment. Their repositories, their standups, sometimes their customer calls. Azumo is SOC 2 certified, client code stays in client repositories, and some engagements carry additional requirements such as HIPAA

How We Work

Our engineers build with AI every day. Claude Code, Codex, and similar tools are part of the standard toolchain here, not an experiment. We run an automated audit across the whole codebase on day one and every day after, grading security, cost, and architecture findings by severity with the exact file and line, so a small team can move quickly without quality drifting. We stay vendor-neutral across OpenAI, Anthropic, and open-weight models, and we run Valkyrie, our own production layer, when a single interface to any model is the right call.

About Azumo

Azumo is a San Francisco based software development company that has been building intelligent applications since 2016. We provide nearshore AI engineering teams to organizations that need production AI faster than they can hire for it: as an embedded engineering team, as AI staff augmentation alongside an existing team, or as a full project build. Our engineers work from Latin America, aligned to United States time zones, and have delivered for Twitter, Meta, Discovery Channel, Omnicom, UnitedHealth, and CENTEGIX.

We hire for seniority and test for it before anyone joins a client team. We support engineers in going deep on the modern AI stack, and we give time back to open-source work, community teaching, and philanthropy.

Requirements

Basic qualifications

• 5+ years building and operating production data pipelines, with Python and SQL as your primary languages, plus the engineering fundamentals that go with it: testing, code review, CI/CD, Git, containers, and orchestration
• Deep expertise in designing and building data warehouses or lakehouses, including dimensional modeling, incremental processing, and the cost and performance trade-offs behind each choice
• Distributed processing at production scale with Spark, Kafka, Flink or equivalent, including the failure modes that only appear under load
• Orchestration as an engineering discipline rather than a cron replacement: Airflow, Dagster or Prefect, with retries, idempotency and backfill strategy you can defend
• Transformation under version control, with tests and lineage: dbt or something you built yourself
• Cloud deployment experience, Azure preferred and AWS acceptable, with Docker, CI/CD pipelines, and infrastructure as code (GitHub Actions, Terraform, or Bicep)
• Working discipline around pipeline cost, runtime and throughput. You can explain what a pipeline costs to run and what you did about it
• Active use of AI-assisted coding tools such as Claude Code, Cursor, or GitHub Copilot in real delivery work
• Clear written and spoken English, C1 or above, and the confidence to explain a technical trade-off directly 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 or Azure AI Search, and the retrieval-quality problems that come with it
• Streaming, real-time or high-throughput workloads
• Experience with cloud-based managed services like Airflow, Glue, Elastic stack, Amazon Redshift, Snowflake, BigQuery, Azure SQL Db, EMR, Databricks
• Prior experience with notebooks using Jupyter, Google Collab, or similar
• Delivery under a compliance regime such as SOC 2 or HIPAA
• Contributions to open-source data libraries, published technical writing, or active participation in the data engineering community

Benefits

• Paid time off (PTO)
• U.S. Holidays
• AI Training
• Mentored career development
• Profit sharing
• $US remuneration