Lead Data Engineer
New
O
OZ DigitalData & AI
ArgentinaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 3–5 years
- Required Skills
- ETLMicrosoft Power BITableauDatabricks
Requirements
- 3–5 years of hands-on experience developing in Databricks.
- Relevant Databricks certifications.
- Proven ability to build, optimize, and troubleshoot production-grade ETL pipelines.
- Demonstrated experience designing and implementing medallion data models (raw, curated, trusted zones) in Databricks environments.
- Strong skills in developing dimensional models to support business intelligence, reporting, and single source of truth requirements.
- Advanced proficiency in orchestrating data ingestion, transformation, and integration workflows across multiple systems and formats.
- Ability to understand operational requirements, translate them into technical deliverables, and communicate the business impact of engineering decisions.
- Demonstrated capacity to take initiative, work independently, and drive projects forward in ambiguous or evolving contexts.
- Clear communication skills with the confidence to provide feedback and advocate for best practices.
- Experience with Azure Data Lake, Data Factory, and related services is a strong advantage.
- Experience with human centric design principles with data visualization tools such as Power BI, Qlik, or Tableau is an advantage.
Responsibilities
- Serve as the technical lead for the project—owning solution design decisions, guiding implementation standards, and mentoring other engineers through coaching, reviews, and knowledge sharing.
- Design, develop, and maintain complex ETL pipelines using Databricks, ensuring scalable, high-performance data integration across multiple source systems.
- Implement and optimize medallion architecture within Databricks, establishing clear data zones (raw, curated, trusted) to support governed, enterprise-wide reporting.
- Develop and refine dimensional data models that enable unified, analytics-ready views of business domains and support automated dashboarding and KPI frameworks.
- Collaborate closely with cross-functional teams (data stewards, IT, business stakeholders) to translate operational requirements into technical solutions, proactively clarifying dependencies and driving alignment.
- Contribute to architectural decisions, leveraging your expertise to recommend best practices, challenge assumptions, and ensure data platform durability and flexibility.
- Identify and address integration challenges, data quality issues, and process bottlenecks early, providing actionable insights and thoughtfully pushing back when project risks or inefficiencies arise.
- Support knowledge transfer and documentation, empowering colleagues and clients to maintain and evolve data solutions independently.
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