About The Position
The Technology Consultant 2 will contribute to software and data engineering work across the software development lifecycle, from requirements gathering and design through coding, testing, implementation, and production support. The role involves Informatica development and Python-based ETL and data test automation, with validation across source-to-target data flows, transformations, databases, and AWS data platforms. The consultant will collaborate with business, development, and data engineering teams, support software integration, document work, and help maintain reliable, high-quality applications and data processes. The role includes facilitating design discussions, documenting business requirements and design decisions, participating in coding and code reviews, maintaining code repositories, implementing test strategies, analyzing results, coordinating defect resolution, and assisting with user training and production issues. The position may require relocation and/or travel to the work or project location. Applicants must be authorized to work in the United States without employer-based visa sponsorship; Infosys does not provide immigration sponsorship for this role. Infosys describes itself as a global digital services and consulting company serving clients in more than 50 countries. The Financial Services unit works with financial institutions on digital transformation using AI, cloud, and data-led innovation.
Requirements
- Experience or skills in Informatica development, Python, AWS, SQL, Oracle, and GitLab.
- Experience with ETL testing, data warehouse testing, and data migration testing.
- Ability to perform Python-based ETL and data test automation for source-to-target validation, reconciliation, and regression testing.
- Ability to write SQL queries and validate Oracle databases.
- Knowledge of source-to-target mapping, transformation rule, data lineage, and business rule validation.
- Knowledge of data reconciliation, completeness checks, referential integrity, duplicate detection, and data quality testing.
- Knowledge of data warehousing concepts, including facts, dimensions, star schema, and SCD validations.
- Experience with AWS data platform validation.
- Experience with GitLab, batch job validation, and workflow monitoring; scheduling tools may include Autosys, Control-M, or Airflow.
- Python or Shell scripting skills for ETL and data validation activities.
- Ability to prepare test strategies, execute test cases, analyze results, and resolve defects.
- Ability to collaborate with business, development, and data engineering teams.
- Ability to document test scenarios, SQL validations, automation assets, and testing processes.
- Bachelor’s degree or foreign equivalent from an accredited institution, or three years of progressive experience in the specialty in lieu of each year of education.
- Authorization to work for any employer in the United States without employer-based visa sponsorship.
Nice To Haves
- BI reporting validation using Power BI, Tableau, Qlik, or MicroStrategy.
- Experience with Big Data technologies such as Hadoop, Spark, Hive, and Kafka.
- Experience with AI-enabled data quality and data governance solutions.
- Experience in banking, capital markets, wealth management, or mortgage domains.
- Exposure to Azure, Databricks, Snowflake, Redshift, PostgreSQL, BigQuery, or GCP.
Responsibilities
- Contribute to requirements elicitation by documenting assigned portions of business requirements under guidance.
- Facilitate software application design discussions and document design decisions for the technical team.
- Participate in coding and integrate new features or updates into existing applications while maintaining system stability.
- Conduct code reviews, make changes to the codebase, and maintain code repositories.
- Implement test strategies, analyze test results, and coordinate bug fixes to uphold software quality standards.
- Develop user training programs, documentation, and support frameworks to support transitions to new software applications.
- Participate in resolving production issues and recommend preventive strategies to improve system reliability.
- Maintain records of code, testing techniques, and support activities for the knowledge base and related projects.
- Perform Python-based ETL and data test automation for source-to-target validation, reconciliation, and regression testing.
- Write SQL queries and validate Oracle databases.
- Validate source-to-target mappings, transformation rules, data lineage, and business rules.
- Perform data reconciliation, completeness checks, referential integrity validation, duplicate detection, and data quality testing.
- Validate data warehousing concepts and structures, including facts, dimensions, star schemas, and SCDs.
- Validate AWS data platforms and batch jobs; monitor workflows and scheduling tools.
- Prepare test strategies and test cases, execute tests, analyze results, and resolve defects.
- Collaborate with business, development, and data engineering teams.
- Document test scenarios, SQL validations, automation assets, and testing processes.
Benefits
- Medical, dental, vision, and life insurance.
- Long-term and short-term disability insurance.
- Health and dependent care reimbursement accounts.
- Accident, critical illness, hospital indemnity, and legal insurance.
- 401(k) plan and contributions dependent on salary level.
- Paid holidays and paid time off.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
