Senior Data Developer
J
JobgetherFinancial services
Remote work within BrazilFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- Extensive experience in data engineering
- Required Skills
- PythonSQLSparkData modelingPySpark
Requirements
- Extensive experience in data engineering, including technical leadership or mentoring of data engineering teams.
- Experience working with financial-sector KPIs and banking-related indicators.
- Advanced SQL skills and strong proficiency in Python and PySpark.
- Hands-on experience with Azure Databricks, including Spark, PySpark, and Spark SQL.
- Practical experience using Azure Data Factory to orchestrate and manage data pipelines.
- Strong knowledge of Medallion architecture, including Bronze, Silver, and Gold layers, and experience with Delta Lake.
- Experience designing and implementing analytical data models, including star schemas, OBT structures, and fact and dimension tables.
- Knowledge of data governance, data quality management, pipeline documentation, and data traceability.
- Experience managing and prioritizing technical backlogs within agile teams.
- Ability to communicate technical concepts to technical and non-technical stakeholders.
- Strong analytical thinking, problem-solving skills, and attention to detail.
- Nice to have: Azure Purview or other data catalog and lineage tools; data CI/CD and Azure DevOps; Power BI or Looker; streaming technologies such as Spark Structured Streaming or Azure Event Hubs; and Azure or Databricks certifications.
Responsibilities
- Provide technical guidance to data engineers and analysts, conduct technical reviews, and promote engineering best practices.
- Design and evolve data platform architecture, defining standards for modeling, pipelines, governance, and Bronze, Silver, and Gold layers.
- Ensure data architecture is consistent, scalable, maintainable, and aligned with business and analytical requirements.
- Support prioritization and refinement of technical demands, backlog items, and user stories.
- Establish data quality practices, including validation, documentation, and traceability across the data lifecycle.
- Develop and review data engineering solutions using SQL, Python, PySpark, and Azure technologies.
- Build, optimize, and orchestrate pipelines with Azure Databricks, Azure Data Factory, Spark, Spark SQL, and Delta Lake.
- Design and maintain analytical models, including star schemas, OBT structures, and fact and dimension models.
- Support reliable delivery of financial-sector KPIs and indicators for reporting and analytics.
- Translate strategic objectives into data solutions and collaborate with stakeholders to improve governance, processes, and platform performance.
View Full Description & ApplyYou'll be redirected to the employer's site
