Senior Data Platform Engineer

About the role

WHY DEPT®?

We are a Growth Invention company built to help the world’s most ambitious brands grow faster. Operating at the intersection of technology and marketing, we create what is next by pioneering ideas, acting fast, and moving further because standing still just is not in our DNA.

We are drawn to people who stay curious, move with intent, and never stop inventing. Our culture runs on three values: better together, relentlessly curious, and get sh*t done. It is how we work, how we grow, and how we make things that matter.

At DEPT®, you will find the freedom to explore, the space to collaborate, and the trust to make a real impact for our clients, for each other, and for the world we are helping to build.

THE ROLE:

We are hiring hands-on Senior Data Engineers in Argentina to join the data engineering squads we have in place at one of our clients. This is not a marketing or e-commerce analytics role. The work is about getting to grips with complex, real-world datasets and turning them into reliable, well-modeled data.

WHAT YOU’LL DO:

- Build, run and maintain data pipelines end to end, from ingestion through transformation to modeled, analysis-ready tables.

- Write and maintain dbt models and SQL transformations on Databricks or Snowflake.

- Design data models and be able to explain, and defend, what a good model looks like. Push back when a design will not hold up.

- Own platform operations, including monitoring, alerting, incident response, and capacity planning, to ensure the platform remains resilient, observable, and cost-efficient in production.

- Design reusable orchestration patterns with Airflow, Databricks Workflows or AWS services such as Glue, Step Functions and S3, with clear ownership, retries and failure handling instead of one-offs per pipeline.

- Contribute to CI/CD for the data platform, focusing on automated testing, environment promotion, and deployment pipelines for dbt model, jobs, and infrastructure-as-code to ensure changes are shipped safely and can be repeated reliably.

- Get to know complex real-world datasets, such as people and employment data or financial data, and understand what they represent.

- Solve the problems that only show up at very large data volumes: partitioning and file-layout strategy, compute right-sizing, job concurrency, and pipeline reliability at scale. Keeping pipelines reliable, performant and cost-aware.

WHAT YOU BRING:

- 5–7 years of experience as a data engineer, including significant time spent building or operating a data platform rather than just running pipelines on top of one.

- Strong SQL

- Hands-on experience with Databricks and/or Snowflake.

- Experience with AWS data services such as S3, Glue, Step Functions, Lambdas

- Experience with orchestration tools such as Airflow or Databricks Workflows, and with building reusable workflows that scale across many pipelines.