Lead the design, development, and evolution of data pipelines, transformations, and reporting applications that deliver accurate, timely insights
Drive modernization of legacy data platforms toward cloud-based and scalable solutions
Integrate AI-enabled capabilities to improve pipeline health, anomaly detection, and data quality
Ensure operational excellence by improving reliability, reducing incidents, and guiding root-cause analysis
Champion Agile, DevOps, and iterative delivery practices that emphasize automation, observability, and continuous improvement
Build and lead high-performing engineering teams by coaching, developing, and empowering engineers and QA professionals
Foster a culture of ownership, collaboration, and learning, including ongoing AI skill development
Partner with product, reporting, analytics, and business stakeholders to translate needs into clear engineering outcomes
Collaborate with governance, QA, and design partners to meet quality, client, and regulatory expectations

Minimum 10 years of experience in software or data engineering
At least 5 years of experience leading and developing high-performing engineering teams
Strong expertise in SQL, ETL frameworks, data modeling, and cloud-based data pipelines
Hands-on experience with platforms such as AWS, Snowflake, Databricks, Teradata, or enterprise BI tools
Proven ability to improve system performance, reliability, and production support processes
Experience working with cross-functional partners across engineering, QA, architecture, and business teams
Bachelor's degree in computer science, engineering, or a related field

Senior Software Engineering Manager

Key skills