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Mid-senior data role with common skills in a metro and broad requirements yields moderate applicant competition.
Requires ESG/Banking domain experience and data analytics expertise, limiting cross-industry transferability.
Explicit 7+ years and mandatory SQL/PySpark/Python plus domain (ESG/Banking) experience create high screening strictness.
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Lead and manage a multidisciplinary team of data, analytics, technology, and delivery professionals to implement advanced data and analytics solutions.
Oversee the delivery of business use cases globally and coordinate deployment of global initiatives locally, ensuring adherence to Agile methodologies.
Manage project progress via Jira, maintain milestones, communicate status to stakeholders, resolve risks and blockers, and ensure deliverables meet business requirements and quality standards.
7+ years experience as a Lead Data Analyst with decision-making, analytical, and problem-solving skills.
Proficiency in SQL, PySpark, Python with experience in ESG or Banking domains.
Hands-on experience with HQL or Spark SQL for data exploration; strong documentation skills including data mapping, subsystem and technical design, and business requirements.
Graduate in Computer Science, Data Science, or related field; 3-4 years experience in data engineering or related field.
Experienced in leading cross-functional teams and managing delivery in Agile frameworks with strong process enforcement.
Skilled in stakeholder management across multiple geographies and disciplines including data scientists, data engineers, and analysts.
Capable of making key implementation decisions, coordinating complex projects with multiple dependencies, and ensuring on-time, quality delivery aligned to strategic Data & Analytics objectives.