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About the role

Overview: In this role you contribute to delivering AI-driven data and software solutions for financial services. You will work on requirements elicitation, design discussions, and end-to-end development to ensure scalable, reliable applications. You collaborate with cross-functional teams to translate complex requirements into actionable tasks and maintain high quality through testing and documentation. You will help production support efforts and share knowledge to strengthen the team's capabilities. This position offers exposure to cutting-edge data and AI technologies within the Infosys Financial Services unit. Compensation / Benefits Medical/Dental/Vision/Life Insurance Long-term/Short-term Disability Health and Dependent Care Reimbursement Accounts 401(k) plan Paid holidays and Paid Time Off
Responsibilities: Document assigned portions of business requirements guided by the project framework Facilitate design discussions and capture design decisions for the build Code and integrate new features into existing applications with stability in mind Perform code reviews and manage changes in code repositories Implement and coordinate test strategies, analyze results, and drive bug fixes Develop user training, documentation, and support materials for smooth adoption Resolve production issues and suggest preventive improvements for reliability Maintain detailed records of code, testing, and support activities to enrich knowledge bases
Key requirements: Understanding of data warehousing testing concepts (facts, dimensions, star schema, SCD validations) Experience building and maintaining scalable ETL/data pipelines using Spark/PySpark/Spark SQL and Python with end-to-end automation Strong SparkSQL and database validation across Oracle, SQL Server, PostgreSQL, Snowflake, AWS Redshift or BigQuery Python-based automation for ETL pipeline execution, data ingestion, transformation, and workflows Experience with ETL tools (Informatica, Ab Initio, ODI, SSIS, DataStage, Azure Data Factory, Databricks) Automated data quality controls within Spark pipelines (reconciliation, schema validation, data profiling, exception handling) for large financial datasets Validation of cloud data platforms on AWS/Azure/Databricks/Snowflake/GCP Batch job validation, workflow monitoring, and scheduling tools (Autosys, Control-M, Airflow) Develop automation solutions using Python/Shell scripting for ETL and data validation Collaborative spirit Excellent communication Ability to translate complex requirements into actionable tasks Spark/PySpark, Spark SQL Python Data warehousing concepts

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JOB OVERVIEW

Experience level

Senior

Location

Richardson, TX

Occupation

Computer Systems Analysts

Industry

Custom Computer Programming Services

Posted

today

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