
Python Data Engineer
5 days ago
A Data Engineer with Python and Azure Databricks is responsible for designing, building, and maintaining scalable and robust data pipelines and data solutions within the Azure ecosystem, leveraging Databricks and Python for development.
Key Responsibilities:
- Data Pipeline Development:
- Design, develop, and maintain efficient and scalable ETL/ELT processes using Azure Databricks, PySpark, and Python to ingest, transform, and load data from various sources into data lakes, data warehouses, or other analytical systems.
- Azure Databricks Expertise:
- Utilize Databricks features, including Delta Lake, notebooks, and optimized Spark clusters, to build and manage data processing workflows.
- Data Integration:
- Integrate data from diverse sources such as relational databases, APIs, cloud storage, and streaming platforms, ensuring data quality and consistency.
- Performance Optimization:
- Optimize data pipelines and processing workflows for performance, efficiency, and cost-effectiveness within the Azure environment.
- Data Governance & Security:
- Implement data quality checks, ensure data security and compliance with relevant regulations, and manage access controls.
- Collaboration:
- Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver tailored solutions that support business needs.
- Troubleshooting & Support:
- Monitor data pipeline performance, troubleshoot issues, and provide ongoing support and maintenance for data solutions.
- Documentation:
- Create and maintain technical documentation, including data models, pipeline designs, and best practices.
Required Skills and Qualifications:
- Programming Proficiency:
Strong expertise in Python and PySpark. - Azure Platform Knowledge:
In-depth understanding of Azure services relevant to data engineering, including Azure Databricks, Azure Data Lake Storage, Azure Data Factory, and potentially Azure Synapse Analytics. - Big Data Technologies:
Experience with Apache Spark and distributed computing concepts. - Data Warehousing & ETL:
Solid understanding of data warehousing principles, data modeling, and ETL/ELT processes. - SQL Skills:
Proficiency in SQL for data manipulation and querying. - Problem-Solving:
Excellent analytical and problem-solving skills to address complex data challenges. - Communication:
Strong communication and collaboration skills to interact effectively with technical and non-technical stakeholders.
Preferred Qualifications (Optional):
- Relevant certifications (e.g., Databricks Certified Data Engineer Associate/Professional, Microsoft Certified: Azure Data Engineer Associate).
- Experience with other data tools or platforms (e.g., Apache Kafka, Snowflake, Power BI).
- Familiarity with Agile development methodologies.
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