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Python Developer
2 months ago
Experience:
o 6-10 years of hands-on experience in SQL and Python development.
o Proven track record of building complex data validation and transformation projects using both SQL and Python.
• Industry Experience:
o Demonstrated experience working in a financial services company, with a focus on life insurance sector.
• Technical Skills:
o Strong practical knowledge in SQL and Python, with an emphasis on creating reusable code.
o Strong skills in data manipulation tools and libraries (e.g., Pandas, NumPy) for cleaning, transforming, and analyzing data.
o Experience with both relational (e.g., SQL Server) and NoSQL databases (e.g., CosmosDB, MongoDB).
o Experience with data integration tools and techniques, including ETL (Extract, Transform, Load) processes, data pipelines, and orchestration (e.g., Azure Data Factory).
o Proficient in writing base code that dynamically generates multiple code instances based on a configuration file (JSON format/SQL database format).
o Experience working in Azure cloud environment, particularly with data lakes.
o A solid knowledge and experience in well-architected frameworks for data platforms in the cloud.
o Familiarity with writing SQL or Python code for accessing and processing data in a data lake, including knowledge of delta and parquet formats.
o Demonstrated experience and skills in data modelling.
o Knowledge of Databricks, Spark, or similar frameworks for processing large datasets is considered an added advantage.
o Knowledge of Apache Spark, including Spark SQL, DataFrame/Dataset API.
o Proficiency in setting up and managing Databricks clusters, notebooks, and jobs within the Azure ecosystem.
o Understanding of Azure security best practices and compliance standards relevant to data engineering workflows in Azure Databricks.
o Designing, implementing, and optimizing end-to-end data pipelines using Azure Databricks, ensuring reliability, scalability, and performance.
o Utilizing Azure Databricks for data transformation tasks including cleansing, aggregation, and enrichment of datasets.
o Implementing automation for data workflows and monitoring pipelines to ensure data integrity and availability.
o Familiarity with version control systems like Git for managing code and notebooks.
