Tech Lead – Data Engineering

New

JobgetherAI, mobility infrastructure

Based in BrazilFull-TimeLead

Salary not disclosed

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Job Details

Bring solid professional experience in data engineering, including acting as a technical reference or lead within an engineering squad.
Have hands-on production experience with Databricks, PySpark, and Spark SQL.
Know medallion architecture, including landing, bronze, silver, and gold layers.
Have experience with Delta Lake, Unity Catalog, and Databricks Workflows.
Have experience preparing and managing data for generative AI applications, including embeddings, vector indexes, data quality, and version control.
Be able to integrate data with APIs and external services while maintaining security and observability.
Demonstrate data modeling skills, particularly across silver and gold analytical layers.
Be familiar with Git, CI/CD pipelines, code reviews, automated testing, and data quality validation.
Have experience working in agile product teams and delivering improvements incrementally.
Have a degree or formal education in Information Technology or a related field.
Nice to have: Experience with Databricks Genie and enterprise generative AI implementation standards.
Nice to have: Knowledge of AI data governance and Brazilian data protection requirements (LGPD).
Nice to have: Experience with Oracle, GoldenGate/CDC replication, SAP data extraction through Open Hub, SQL Server, or Microsoft Graph.
Nice to have: Familiarity with Power BI, service identities, secrets management, and Confluence.

Take technical ownership of product requirements and delivery by understanding users and underlying data sources.
Build, maintain, and optimize data pipelines using PySpark and SQL within Databricks.
Implement and evolve medallion architecture across landing, bronze, silver, and gold layers.
Use Delta Lake, Unity Catalog, and Databricks Workflows to support reliable data operations.
Prepare and manage datasets for generative AI services, including embeddings, vector indexes, data quality, and versioning.
Integrate data with AI services, Databricks Genie, APIs, and external services.
Design and maintain the gold data layer for trusted datasets used by the product, Power BI, and analytical applications.
Develop reusable engineering frameworks, automated validations, and data quality controls.
Apply Git versioning, CI/CD, automated testing, code reviews, and technical documentation.
Collaborate with product teams, guide squad members, and work with Data Governance teams on classification, anonymization, lineage, and access controls.

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