Data Analyst - Senior Manager

As a Data Analyst, Senior Specialist at EY, you will play a crucial role in driving data-driven decision-making across the organization. Your main responsibilities will include leading complex analytical projects, optimizing data processes, and delivering actionable insights. Here are your key responsibilities: - Data Validation: Perform quality assurance on investment data for reports, fact sheets, and participant tools. - Benchmark Mapping: Maintain and update benchmark and peer-average associations for funds. - Cross-Functional Alignment: Collaborate with different teams to ensure data quality across platforms. - Issue Resolution: Investigate and resolve data defects in collaboration with application owners. - Documentation: Update data dictionaries, QA playbooks, and mapping rules. - External Feeds: Validate data sent to external providers for accuracy and compliance. To excel in this role, you should possess the following skills and attributes: Base Skillsets: - Deep knowledge of investment products, benchmarks, and data structures. - Experience working with custodians and sending investment data to aggregators. - Strong attention to detail and analytical skills. - Strong Data Governance background. - Proficiency with data tools like SQL and ability to implement QA automation. Technical Expertise: - Advanced SQL skills and intermediate Python knowledge. - Advanced visualization skills using tools like Tableau. - Deep understanding of experimental design techniques. - Consistent code version control practices. - Ability to identify opportunities for advanced analytical methods. Product Ownership: - Influence strategy and drive long-term strategic roadmap. - Connect ecosystem of data elements to serve multiple workstreams. - Advanced domain expertise. - Maintain and own various business products. Project & Stakeholder Management: - Ability to influence senior stakeholders and manage complex initiatives. - Effectively communicate complex concepts and solutions. - Proactively identify and solve issues. - Ensure alignment and clarity of goals with partners. To qualify for this role, you must have: - Minimum 13+ years of relevant work experience in advanced analytics. - Bachelors degree in Computer Science, IT, or Statistics/Mathematics. - Expertise in SQL, Python or R, and cloud-based data platforms. - Strong analytical, communication, and interpersonal skills. - Constantly updating yourself about new technologies. In addition to the qualifications, strong teamwork, work ethic, product mindset, client centricity, and commitment to EY values are essential for success in this role. EY offers a competitive remuneration package and comprehensive Total Rewards, including support for flexible working, career development, coaching, and feedback. Join EY in building a better working world through trust, long-term value creation, and technological innovation. As a Data Analyst, Senior Specialist at EY, you will play a crucial role in driving data-driven decision-making across the organization. Your main responsibilities will include leading complex analytical projects, optimizing data processes, and delivering actionable insights. Here are your key responsibilities: - Data Validation: Perform quality assurance on investment data for reports, fact sheets, and participant tools. - Benchmark Mapping: Maintain and update benchmark and peer-average associations for funds. - Cross-Functional Alignment: Collaborate with different teams to ensure data quality across platforms. - Issue Resolution: Investigate and resolve data defects in collaboration with application owners. - Documentation: Update data dictionaries, QA playbooks, and mapping rules. - External Feeds: Validate data sent to external providers for accuracy and compliance. To excel in this role, you should possess the following skills and attributes: Base Skillsets: - Deep knowledge of investment products, benchmarks, and data structures. - Experience working with custodians and sending investment data to aggregators. - Strong attention to detail and analytical skills. - Strong Data Governance background. - Proficiency with data tools like SQL and ability to implement QA automation. Technical Expertise: - Advanced SQL skills and intermediate Python knowledge. - Advanced visualization skills using tools like Tableau. - Deep understanding of experimental design techniques. - Consistent code version control practices. - Ability to identify opportunities for advanced analytical methods. Product Ownership: - Influence strategy and drive long-term strategic roadmap. - Connect ecosystem of data elements to serve multiple workstreams. - Advanced domain expertise. - Maintain and own various business products. Project & Stakeholder Management: - Ability to influence senior stakeholders and manage complex initiatives. - Effectively communicate complex concepts and solutions. - Proactively identify and solve issues. - Ensure alignment and clarity of goals wit