Senior Data Analyst

Requirements: Min 8 years of experience. At least 8 years of data-related working experience, with at least 5 years of hands-on experience in data management and data analytics in the finance, investment, banking, and asset management industries. Good understanding of the principles, key controls and processes relating to data management. Good at working with details and is meticulous in operations and tasks. Good understanding of data modelling. Good understanding on OLTP, Data Lake, Lakehouse technologies that may include knowledge of Snowflake, S3, AWS Glue, DeltaLake, DataBricks. Good understanding of CI/CD pipelines, DevOps, DataOps, and their applicability in financial services. Experience in working with databases using database technologies (e.g. MS SQL, Snowflake, Redshift and PostgreSQL) and data integration products (e.g. Informatica, Data Factory, Bash). Extensive data-related experience in programming languages eg Python, SQL, Scala, Javascript, and similar. Experience in working with business intelligence and data visualization tools e.g. Tableau, PowerBI. Experience with SDLC methodology and/or agile methodologies like Scrum and Kanban and its related applications eg Confluence and JIRA. Good team player, with strong analytical skills and enjoys complex problem solving. Experience in data engineering platforms and virtualization eg Denodo, Jupyter, O365. Excellent written and spoken English and strong presentation skillsets. Strong inter-personal and people leadership skills to interact with diverse stakeholders. Responsibilities: Working closely with data analysts and business end-users to implement and support data platforms using best-of-breed technology and methodology. Implement and design robust and scalable solutions to meet business needs and take operational considerations into account. Demonstrate technical expertise in the assigned areas. Manage operations and maintain SLAs. Implement automation in data management. Collaborate with data engineering, architecture, and governance. Perform data and metadata quality, modeling, lineage, cleansing, onboarding, registration, discoverability, access controls, migration, optimization, and cataloging. Execute, maintain and manage the whole data lifecycle. Conduct requirement workshops with stakeholders, analyze and translate business and other requirements holistically into data strategies, plans, and actions. Cover all aspects of data distribution and data service. Design and implement scalable data pipelines and data service modules with robustness in mind to support the growing demands from business users. Analyze and resolve day-to-day operational incidents and advisory to business users. Listen to upstream changes, perform impact analysis and mitigate-modify-implement. Do impact analysis, inform and distribute to downstream users and applications. Analyze systems operations data (SLAs, customer satisfaction, delivery quality, team efficiency etc.) to identify actionable trends for continual improvements. Data, application, and system checking and testing (eg UAT), CICD and go-live. #J-18808-Ljbffr