Data Ops Capability Deployment - Analyst

• Perform data analytics and data analysis across different asset classes
• Build data science/Tooling capabilities
• Work closely with the wider Enterprise Data team, in particular the front to back leads
• Deliver business priorities
• Address performance bottlenecks
• Design batch orchestrations
• Deliver Reporting capabilities
• Build analytics dashboards & data science capabilities
• Communicate findings and propose solutions
• Convert business requirements into technical design documents
• Prepare handover documents
• Manage SIT, UAT and Implementation

• Hands on with data engineering background and have thorough understanding of Distributed Data platforms and Cloud services
• Sound understanding of data architecture and data integration with enterprise applications
• Research and evaluate new data technologies, data mesh architecture and self-service data platforms
• Work closely with Enterprise Architecture Team on the definition and refinement of overall data strategy
• Should be able to address performance bottlenecks, design batch orchestrations, and deliver Reporting capabilities
• Ability to perform complex data analytics (data cleansing, transformation, joins, aggregation etc.) on large complex datasets
• Build analytics dashboards & data science capabilities for Enterprise Data platforms
• Communicate complicated findings and propose solutions to a variety of stakeholders
• Understanding business and functional requirements provided by business analysts and convert into technical design documents
• Work closely with cross-functional teams e.g. Business Analysis, Product Assurance, Platforms and Infrastructure, Business Office, Control and Production Support
• Prepare handover documents and manage SIT, UAT and Implementation
• Demonstrate an in-depth understanding of how the development function integrates within overall business/technology to achieve objectives
• requires a good understanding of the banking industry
• Experience with Data Quality/Data Tracing/Data Lineage/Metadata Management Tools
• Hands on experience for ETL using PySpark on distributed platforms along with data ingestion, Spark optimization, resource utilization, capacity planning & batch orchestration
• In depth understanding of Hive, HDFS, Airflow, job scheduler
• Strong programming skills in Python with experience in data manipulation and analysis libraries (Pandas, Numpy)
• Should be able to write complex SQL/Stored Procs
• Should have worked on DevOps, Jenkins/Lightspeed, Git, CoPilot
• Strong knowledge in one or more of the BI visualization tools such as Tableau, PowerBI
• Proven experience in implementing Datalake/Datawarehouse for enterprise use cases
• Exposure to analytical tools and AI/ML is desired

Exposure to analytical tools and AI/ML

• Equal opportunity employer
• Potential career growth
• Commitment to employee well-being