Senior Data Engineer
New
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Upper-intermediate English proficiency.
- Experience
- 5+ years
- Required Skills
- PythonSQLETLSparkPySpark
Requirements
- 5+ years of experience in senior data engineering roles.
- Deep understanding of cloud-based data platforms and pipeline architecture.
- Advanced proficiency in SQL, Python, Spark, and PySpark.
- Strong experience with ETL/ELT concepts, schema evolution, and orchestration.
- Experience designing and implementing medallion, lakehouse, or warehouse data architecture patterns.
- Hands-on experience with Microsoft Fabric, Microsoft Foundry, APIM Gateway, App Insights, and Cosmos DB.
- Familiarity with AI and machine learning data requirements and governed data access.
- Strong knowledge of data quality management, performance optimization, and troubleshooting.
- Proven leadership abilities, including mentoring, coaching, and team development.
- Ability to manage multiple workstreams and balance delivery priorities.
- Upper-intermediate English proficiency with strong written and verbal communication skills.
- Experience with Databricks, Snowflake, or BigQuery is a plus.
Responsibilities
- Lead complex data engineering initiatives across multiple business areas, source systems, data domains, and stakeholder groups.
- Define and implement scalable data engineering strategies aligned with business needs and downstream consumption goals.
- Design and improve data ingestion, transformation, orchestration, and serving solutions across cloud warehouses and lakehouses.
- Develop structured pipeline strategies, curated data layers, and reusable workflows supporting analytics, AI, and agentic use cases.
- Establish and promote engineering best practices including schema design, data validation, lineage, and maintainable architectures.
- Improve data quality, strengthen source-system alignment, and increase trust in governed datasets.
- Collaborate with cross-functional teams including Data Scientists, AI Engineers, and Architects to align data foundations with objectives.
- Mentor and develop data engineering professionals through coaching, feedback, and technical guidance.
- Review technical deliverables to ensure quality, consistency, and scalability.
- Ensure compliance with governance, privacy, and security standards across all engineering activities.
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