Senior Data Engineer

New

ArionkoderData engineering

Location: LATAMContractSenior

Salary not disclosed

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Job Details

Languages: C1 English
Experience: 6+ years of professional data engineering; 3+ years hands-on with Databricks in production, including Unity Catalog
Required Skills: PythonSQLRESTful APIsData modelingDatabricks

Have 6+ years of professional data engineering experience building production-grade pipelines and data models.
Have 3+ years of hands-on Databricks experience in production, including Unity Catalog.
Use expert SQL and Python to write, debug, optimize, and refactor pipelines independently.
Have strong data-modeling experience, including SCD Type 2, effective dating, or temporal validity.
Design provenance and lineage into data models.
Apply data-quality testing and expectations in pipelines, using dbt tests, Great Expectations, or similar, and CI/CD for data.
Have experience reconciling data across systems through entity resolution or master data.
Have experience with incremental and change-driven processing, such as CDC, streaming, or incremental frameworks, against freshness targets.
Have implemented production access control, such as row-level security, column masking, or role- or attribute-based access.
Understand where platform-level controls stop and application-level enforcement must begin.
Have built APIs or services that serve data.
Work autonomously, challenge unclear definitions, prioritize by risk, and own outcomes without micromanagement.
Use AI coding tools daily, such as Claude or Cursor, with judgment about when to trust their output.
Have C1 English and be comfortable working fully remotely and directly with a client team.

Build production-grade data pipelines and models on Databricks.
Reconcile business entities across systems that use different identifiers.
Model facts with their source, provenance, lineage, and validity period.
Turn business definitions into certified tables.
Implement access controls and application-level permission enforcement.
Build APIs or MCP tools that serve data using the caller’s permissions.
Implement incremental or change-driven processing against freshness targets.
Work with business stakeholders and the client’s data platform team, following their conventions and handing over completed work.

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