Software Engineer

Required Qualifications

• Bachelors degree in Computer Science, Engineering, related field, or equivalent relevant work experience.
• 5-7 years of relevant experienced required.
• Deep and broad technical background with 7-10+ years experience of progressive scope/impact/responsibility (including both hands-on and leadership roles) working with relevant enterprise technologies.
• 3-5+ years of project management experience with a proven track record for delivering projects and driving execution.
• Demonstrated experience and success in influencing, leading, and coaching without direct leadership responsibility.
• Demonstrated superior reasoning and troubleshooting skills.
• Technical skill requirements vary per domain.
• Experience in the Asset Management or Financial Services industry with a strong understanding of investment products, sales and redemptions, AUM etc.
• Advanced proficiency in Python for data engineering, analytics, automation, and data quality validation.
• Hands-on experience with Tableau for enterprise reporting, dashboard development, semantic layer design, and self-service analytics enablement.
• Strong experience designing and developing solutions on modern cloud data platforms, including Snowflake and AWS Redshift.
• Deep expertise in data modeling, dimensional modeling, semantic data layers, and analytics-ready data structures.
• Proven ability to design and implement complex data transformation pipelines, integrating data from multiple internal and external sources.
• Experience with Master Data Management (MDM), reference data management, data governance, metadata management, and data lineage practices.
• Strong understanding of enterprise data architecture, including data warehousing, lakehouse concepts, ETL/ELT frameworks, and real-time data processing.
• Experience working with cloud-native AWS services such as S3, Glue Catalog, Lambda, Athena and IAM.
• Familiarity with data catalog and governance platforms such as Alation, Collibra, or Informatica.
• Experience optimizing SQL workloads and performance tuning across large-scale analytical datasets.
• Knowledge of data quality monitoring, reconciliation processes, and automated testing frameworks for analytical data platforms.