• Core Competencies: Programming Languages | Data Warehousing | ETL Tools | Strong Communication Skills | Team Handling
• Solid skills in programming with Python, SQL, and tools like PySpark and Pandas for data manipulation and analysis.
• Well-versed in data warehousing solutions like Snowflake, Redshift, and Google BigQuery to manage large datasets.
• Experienced with ETL tools such as Azure Data Factory, AWS Glue, and Informatica to streamline data workflows.
• Strong communication skills, able to explain complex concepts clearly and collaborate effectively with teams.

• Proficiency in programming languages like Python, SQL, PySpark, and Pandas for data processing and analysis.
• Experience working with data warehousing solutions such as Snowflake, Redshift, Google BigQuery, or RDS.
• Hands-on with ETL tools like Azure Data Factory, AWS Glue, or Informatica for efficient data movement and transformation.
• Strong communication skills and team handling experience for effective collaboration and leadership.
• Familiarity with data validation tools, especially Informatica, to ensure data quality and accuracy.
• Knowledge of cloud services, particularly AWS Lambda, for serverless computing and automation.
• Experience with data transformation tools like dbt (Data Build Tool) to optimize data pipelines.
• Proficient with BI tools like Power BI, Tableau, or similar for creating impactful data visualizations and reports.

• Design and run test cases for ETL processes, making sure the data is accurate, transformation logic works, and mappings are validated.
• Validate data migration between systems like data warehouses and data lakes to ensure it’s complete, consistent, and accurate.
• Use SQL and other tools to test and validate large datasets, optimizing queries and stored procedures for better performance.
• Automate repetitive ETL tasks with tools like Python, PySpark, and Pandas to cut down on manual work and improve efficiency.
• Validate data models and ensure they align with business needs and technical specs, making sure everything fits together.
• Perform data profiling, quality checks, and analysis to spot trends or issues that could affect data quality.
• Collaborate with ETL developers and data engineers to troubleshoot and fix issues, documenting defects and keeping everyone in the loop for quick resolution.

• The opportunity to drive data accuracy and performance improvement through the design and execution of thorough test cases, ensuring high-quality ETL processes.
• The chance to optimize workflows by automating repetitive tasks, making the data validation process more efficient and reducing manual efforts.
• The ability to collaborate with teams to troubleshoot issues, identify root causes, and enhance the overall performance of data systems.
• A chance to stay updated with the latest ETL best practices, tools, and technologies to continuously improve data integration and testing processes.
• The satisfaction of ensuring data integrity and consistency across systems while contributing to the success of data-driven initiatives.
• Other Level based benefits

Explore and find out why we are made for each other: Life at OptiSol

Read and discover: Life at OptiSol – Medium

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