Technology Project Manager

Infosys•Richardson, TX

About The Position

The Technology Project Manager, assigned the role of Technology Project Lead 2, leads client-aligned technology projects for Infosys’s Financial Services unit. The role defines project objectives and scope in line with business goals, manages delivery with stakeholders, maintains client relationships, and communicates project status. It also drives process improvement, identifies and mitigates risks, manages estimates, budgets, and resources, supports technical and commercial proposals, and oversees scope changes and client requirements, including estimation, approval, and contractual updates. The position requires substantial data testing and validation expertise. Responsibilities include validating ETL, data warehouses, data migrations, cloud platforms, batch workflows, and large-scale financial datasets; building Spark- and Python-based validation and automation solutions; and ensuring data quality, completeness, accuracy, and consistency. The role works with business, development, and data engineering teams, coordinates defect resolution, documents testing processes, and supports team performance and professional development. The Infosys Financial Services unit works with financial institutions on digital transformation using AI, cloud, and data-led innovation across banking, wealth management, and insurance. The position may require relocation and/or travel to a work or project location. A bachelor’s degree or foreign equivalent from an accredited institution is required; three years of progressive specialty experience may substitute for each year of education.

Requirements

Experience in ETL data quality testing, data warehouse testing, and data migration testing.
Expertise in SparkSQL and database validation across Oracle, SQL Server, PostgreSQL, Snowflake, AWS Redshift, or BigQuery.
Experience validating source-to-target mappings, transformation rules, data lineage, and business logic against mapping documents.
Experience in Python-based big data and ETL test automation.
Experience performing data reconciliation, completeness checks, referential-integrity checks, duplicate detection, and data quality validations.
Experience with ETL tools such as Informatica, Ab Initio, ODI, SSIS, DataStage, Azure Data Factory, or Databricks.
Understanding of data warehousing concepts, including facts, dimensions, star schemas, and SCD validations.
Experience designing and implementing Spark-based data validation frameworks for large-scale financial data across batch and ETL pipelines.
Experience developing automated data quality controls and validation test suites using Apache Spark for high-volume financial datasets.
Experience validating cloud-based data platforms on AWS, Azure, Databricks, Snowflake, or GCP.
Experience with batch job validation, workflow monitoring, and scheduling tools such as Autosys, Control-M, or Airflow.
Experience developing automation solutions using Python or Shell scripting for ETL and data validation.
Experience implementing test strategies, preparing and executing test cases, analyzing results, and coordinating defect resolution.
Experience collaborating with business, development, and data engineering teams.
Experience maintaining detailed documentation of testing scenarios, SQL validations, automation assets, and processes.
Bachelor’s degree or foreign equivalent from an accredited institution, or three years of progressive specialty experience in lieu of each year of education.
Applicants must be authorized to work in the United States.

Nice To Haves

Experience in BI reporting validation using Power BI, Tableau, Qlik, or MicroStrategy.
Experience with big data technologies such as Hadoop, Spark, Hive, and Kafka.
Knowledge of AI-enabled data quality and data governance solutions.
Banking, capital markets, wealth management, or mortgage domain experience.

Responsibilities

Define project objectives and scope in alignment with business goals and coordinate delivery with stakeholders.
Build and maintain client relationships and provide regular project updates.
Review project deliverables and improve processes using appropriate methodologies and tools.
Identify project risks and implement mitigation strategies.
Develop project estimates, monitor budget performance, and optimize resource allocation.
Support preparation of technical and commercial proposals aligned with client needs and organizational capabilities.
Participate in resource planning, conduct performance appraisals, and facilitate professional development.
Manage scope changes and develop client requirements, including estimation, approval, and contractual updates.
Design and implement Spark-based data validation frameworks for large-scale financial data across batch and ETL pipelines.
Develop automated data quality controls and test suites, including reconciliation, schema validation, profiling, and exception handling.
Validate source-to-target mappings, transformation rules, data lineage, and business logic against mapping documents.
Perform data reconciliation, completeness checks, referential-integrity checks, duplicate detection, and other data quality validations.
Implement test strategies; prepare and execute test cases; analyze results; and coordinate defect resolution.
Validate batch jobs and monitor workflows and schedules.
Collaborate with business, development, and data engineering teams to ensure reliable data delivery.
Maintain documentation of test scenarios, SQL validations, automation assets, and testing processes.
Foster team performance and growth and support a culture of learning and innovation.