Lead Data Analyst

Must-Have:

• 7+ years of professional experience as a Lead Data Analyst with good decision-making, analytical and problem-solving skills.


• SQL, Pyspark, Python with ESG / Banking Domain experience.


• Hands-on experience in HQL or SQL (Spark SQL) for Data exploration.


• Documentation - Data Mapping, Subsystem Design, Technical Design, Business Requirements.


• Exposure to Logical to Physical Mapping, Data Processing Flow to measure the consistency, etc. 


• Understanding of ER Diagram and Data Modelling concepts


• Exposure to Data Management, Data Cleaning, Data Quality and Data Preparation


• Exposure to working in Agile framework.

Role & Responsibilities:

• Should have worked on Jira tickets creation/maintained/closed and update the tickets with delivery updates aligned to the delivery sprint cadence.


• Help to ensure L1 & L2 milestones are maintained, and statuses reflected in dashboards and monthly reporting


• Lead a highly skilled, multi-disciplinary team of consisting of data, analytics, technology and delivery professionals to deliver advanced data and analytics solutions


• Act as Lead DA and Pod Lead for teams to deliver business use cases on a global scale and deploying locally.


• Coordinate the deployment of global initiatives into markets.


• Working closely with data, analytics and technology teams across the business, shape solution designs which meets business needs as well as aligning to the broader strategic Data & Analytics objectives


• Work directly with various stakeholders across multiple geographies, such as technical discipline leads, data scientists, data engineers and data analysts to produce accurate delivery estimates and manage the transition from analysis to successful delivery.


• Define Agile project working approach and enforce all team members to follow the same methodology.


• Ensure awareness, involvement and support from key stakeholders by building strong pod teams and maintaining robust communications on the project status throughout its lifecycle.


• Ensure risks and issues are identified, managed closely and remove project blockers from the team.


• Make key decisions to ensure the successful implementation of all initiatives.


• Supporting the delivery production line such as definition, implementation and testing and ensuring deliverables meet business requirements and are fit for purpose.


• Drive all stakeholders to deliver on time and to the required quality standards.


• Ensures cross-team delivery plans exist which represent an achievable commitment based on the capacity of available resource and consideration of internal and external dependencies.

Graduate in Computer Science, Data Science, or related field. 3-4 years of experience in data engineering or a related field.