Lead Data Engineering

Mphasis is seeking a Senior Data Engineer to support Fidelity’s Legacy Batch Modernization and Rapid Data Ingestion platform enhancement initiatives. The role focuses on modernizing legacy batch processing workloads and improving cloud-based data ingestion capabilities while ensuring data quality and operational support.

Responsibilities:

• Analyze legacy batch jobs, scripts, database procedures, and data workflows to understand current-state processing, dependencies, business logic, scheduling requirements, and operational constraints
• Rewrite and migrate legacy batch workloads into AWS Batch using modern engineering practices while preserving business functionality and meeting existing service-level agreements
• Develop, enhance, and maintain data pipelines and batch processing solutions using Java Spring Batch, Python, SQL, shell scripting, and AWS services
• Support integration between on-premises legacy systems and AWS-based target platforms, ensuring reliable data movement, synchronization, monitoring, and operational continuity
• Build and optimize data ingestion workflows to support the Rapid Data Ingestion platform, including file intake, validation, transformation, inventory tracking, and downstream consumption
• Implement data quality checks to detect changes in vendor file formats, data integrity issues, missing or incomplete data, and other anomalies that may impact critical business processes
• Enable and support data lineage capabilities by capturing source-to-target data movement, transformations, metadata, and audit-relevant processing details
• Support disaster recovery implementation for the Rapid Data Ingestion platform in AWS to improve resiliency and continuity for critical and audit-sensitive processes
• Develop and maintain job scheduling and orchestration processes using Control-M or equivalent scheduling tools
• Implement secure engineering practices, including application-specific database access, elimination of shared credentials, and enforcement of least-privilege access principles
• Perform performance tuning to ensure rewritten jobs meet or exceed current batch cycle expectations, including completion within strict operational SLAs
• Participate in data validation, reconciliation, defect triage, testing, production deployment, and warranty support activities
• Collaborate with data analysts, systems analysts, architects, application teams, database teams, governance teams, and business stakeholders to ensure successful delivery
• Create and maintain technical documentation covering design, mappings, dependencies, lineage, quality rules, operational procedures, and deployment details

Requirements:

• 9+ years of relevant data engineering experience, preferably in financial services, investment management, banking, or other highly regulated enterprise environments
• Strong experience with Java Spring Batch for batch job development, migration, and modernization
• Hands-on experience with AWS services, especially AWS Batch and Amazon S3
• Strong SQL development experience, including Oracle SQL and PL/SQL concepts
• Experience developing data engineering solutions using Python
• Strong shell scripting experience using Bash, KornShell, or similar Unix/Linux scripting languages
• Experience with Control-M or equivalent enterprise job scheduling and orchestration tools
• Experience working with legacy batch processing environments and modernizing on-premises batch workloads to cloud-based platforms
• Ability to analyze and convert legacy scripts, SQL loaders, stored procedures, and batch workflows into modern, maintainable data processing jobs
• Experience with data ingestion, ETL/ELT development, data validation, reconciliation, and production support
• Understanding of secure access patterns, credential management, least-privilege controls, and audit-focused data processing
• Experience with CI/CD, code deployment, version control, and standard software engineering practices
• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Engineering, or a related field, or equivalent practical experience
• Experience with Snowflake SQL and cloud data warehouse environments
• Working knowledge of Collibra or similar data governance, metadata management, and data lineage tools
• Experience with file inventory management, metadata capture, and source-to-target lineage reporting
• Experience implementing data quality frameworks, validation routines, exception handling, and notification workflows
• Familiarity with AWS disaster recovery patterns and resilient data platform design
• Exposure to financial services data, compensation processes, intermediary business processes, or audit-sensitive workloads
• Experience with legacy technologies such as ksh, Perl, sqlldr, sqlplus, and Oracle-based batch processing
• Familiarity with production incident management, escalation, monitoring, and operational runbooks