Technology Support Engineer

Project Role :
Technology Support Engineer
Project Role Description :
Resolve incidents and problems across multiple business system components and ensure operational stability. Create and implement Requests for Change (RFC) and update knowledge base articles to support effective troubleshooting. Collaborate with vendors and help service management teams with issue analysis and resolution.
Must have skills :
Wealth Management Advisory Solutions
Good to have skills :
Microsoft 365, MySQL, Data Analysis & Interpretation
Minimum 5 Year(s) Of Experience Is Required
Educational Qualification :
15 years full time education
Qualifications – Data Operations Analyst
Required:
Bachelor s degree in Finance, Economics, Data Science, Statistics, Computer Science, or a related field.
3+ years of experience in a data analyst or business analyst role, preferably within financial services or asset management.
Deep domain knowledge of financial instrument reference data, including equities, fixed income, derivatives, and structured products.
Familiarity with industry-standard data sources and vendors (e.g., Bloomberg, Refinitiv, ICE, Markit, Morningstar).
Strong understanding of instrument lifecycle events (e.g., corporate actions, pricing, identifiers like ISIN/CUSIP/Sedol).
Familiarity with investment products, portfolio analytics, or performance attribution.
Strong business acumen and understanding of asset management operations and client needs.
Experience working with large, complex datasets and relational databases.
Excellent communication and stakeholder management abilities.
Key Responsibilities
Serve as the subject matter expert on financial instrument reference data, including equities, fixed income, derivatives, and structured products.
Collaborate with investment operations, compliance, and technology teams to ensure accurate and timely onboarding and maintenance of instrument reference data.
Analyze and validate data from external vendors (e.g., Bloomberg, Refinitiv, ICE) to ensure consistency, completeness, and alignment with internal systems.
Monitor and resolve data quality issues related to instrument identifiers, classifications, pricing sources, and corporate actions.
Develop and maintain data dictionaries, lineage documentation, and metadata for reference data domains.
Partner with data governance teams to define and enforce data standards, policies, and controls for reference data.
Support integration of reference data into downstream systems such as trading platforms, risk engines, and client reporting tools.
Design and implement dashboards and reports to track data quality metrics, exceptions, and operational KPIs.
Participate in projects to enhance reference data architecture, including vendor transitions, system upgrades, and automation initiatives.
Provide training and guidance to business users on reference data usage, best practices, and issue resolution workflows.
Preferred:
Master s degree in a quantitative or business-related field.
Proficiency in SQL and data visualization tools (e.g., Power BI, Tableau).
Experience With Python, R, Or Other Statistical Programming Languages.
Knowledge of data governance, data lineage, and metadata management.
Experience with cloud-based data platforms (e.g., AWS, Azure, Snowflake).
Exposure to Agile or Scrum methodologies in a data or analytics environment.
Ability to translate business questions into analytical frameworks and data models.
Experience with data quality frameworks and regulatory compliance (e.g., SEC, MiFID II).
Ability to manage multiple priorities and deliver high-quality work under tight deadlines.
Strong presentation skills with the ability to explain technical concepts to non-technical audiences.