Especialista de Arquitetura de Dados
Based in BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- Apache AirflowApache KafkaData engineeringSparkDatabricks
Requirements
- Proven experience leading complex projects involving the creation or evolution of Data Platforms.
- Strong technical background in data engineering, with practical experience designing and managing large-scale ETL/ELT pipelines.
- Experience with technologies such as Databricks, Spark, Kafka, Mage, Airflow, or similar data processing tools.
- Advanced knowledge of modern analytical architecture concepts, including lakehouse, data warehouse, streaming, batch processing, open data formats, and analytical modeling patterns.
- Ability to make strategic technical decisions in complex environments with multiple stakeholders.
- Experience designing observability solutions, including metrics, alerts, dashboards, and monitoring strategies for complex systems.
- Strong written and verbal communication skills, with the ability to explain technical concepts clearly.
- Knowledge of cloud infrastructure costs, optimization strategies, and architectural trade-offs between managed and custom solutions.
Responsibilities
- Act as a technical reference across multiple data teams, ensuring consistency, quality, and alignment with product and technology strategies.
- Lead end-to-end data architecture decisions, including lakehouse, data warehouse, streaming, batch processing, and real-time solutions.
- Drive the evolution of data ingestion, processing, quality, and availability pipelines at large scale.
- Ensure operational excellence of critical data pipelines through clear SLAs, observability practices, monitoring, data quality controls, and troubleshooting processes.
- Develop the Data Platform as an internal product, improving self-service capabilities and standardizing engineering practices.
- Mentor data engineers and developers at different experience levels, encouraging technical growth and knowledge sharing.
- Identify and solve structural challenges related to infrastructure costs, processing efficiency, data quality, technical debt, and organizational complexity.
- Serve as a technical partner for Product, Software Engineering, Infrastructure, Security, and business teams.
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