Spec, Fin Plan & Analy : Data Steward
Finance Chief Data Office a data governance team focusing on setting/executing the data strategies across Finance. The team partners with the Data Strategy and Digital groups to roll out the data disciplines. We supports data consumers to drive data quality across Finance functions, including accounting, regulatory and financial reporting, CFO teams and corporate treasury. We aim to provide transparency of our data asset and actively manage the asset to contribute to the fulfilment of regulatory requirements and creation of operational efficiency. The CDO function plays a critical role in the transformation of the organization by connecting data, technology, processes and controls.
Data stewardship is one of the core capabilities of the Finance CDO team to support an efficient and effective Finance wide Data Strategy. The data steward role is a critical role in establishing and monitoring data controls. The role will be a key leader across multiple projects working with Data Architects in understanding architectural directions, gathering and analyzing business requirements, developing the appropriate data controls, and provide guidance and support on data remediation. In addition, the role will have significant influence in Finance Data as a Service initiatives. This position will play a key role in the transition from traditional reporting to a more dynamic, real-time analytics environment ensuring a foundation to deliver information to internal consumers at all levels in the organization.
Data Quality analysis is one of the core capabilities of the Finance CDO team. This staff position is expected to work with large dataset and support the organization to improve the six data quality dimensions through the following activities:
- Define (with the support from Data Champions across Finance) the types of metadata that will be captured and shared, including business, technical, and operational metadata that impacts Finance
- Works closely with technical architects to deliver business architecture artifacts needed for Finance data model, Issue Inventory, Deliverable/reporting inventory, Data transformation inventory, and other identified data assets.
- Consult, facilitate understanding and translate data requirements into logical, physical and semantic layer models across the analytical data environment
- Ensure data structures are designed for flexibility to support future business needs
- Profile and analyze source system data to determine data relationships, design constructs, consistency and quality.
- Enable and guide analytics user community in the understanding, location and selection of appropriate data sources to achieve key business goals
- Ensure that data designs follow architectural best practices and appropriate business rules
- Be an advocate for best practices while balancing business value and reasonable practicality
- Create and maintain critical data documentation and metadata that allows data to be understood and leveraged as a shared asset.
- Analyze and evaluate data definition and modeling environment providing key recommendations for improvement. Assist in defining data modeling standards, and foundational best practices
- Identify gaps and opportunities with regard to data governance and data ownership, and provide recommendations for improvements incorporating best practices
- Facilitate understanding of high quality data management discipline throughout the corporation
- Automate repeatable process for maintaining the Finance Data model
- Advocate and drive adoption of “best practices” to ensure standardization of business architecture outputs across Finance
- Solid functional and business knowledge on Corporate Finance, Regulatory Reporting and Corporate Treasury business process and data model
- SME knowledge of internal and external reporting (ALM/LCR/NSF), especially around data consumption requirements
- Clearly define data requirements in the business context. This includes data sourcing requirements and data quality business rules.
- Collaborate with domain SMEs (Data Champions) to understand data consumptions and data usability
- Construct workflow charts and diagrams; studying system capabilities; writing business requirements
- Define project requirements by identifying project milestones, phases and elements (collaborate with PMO)
- Perform root cause analysis for data quality errors, research and determine scope and complexity of issues and identify the steps for remediation
- Understand and communicate the financial and operational impact of any changes
- Coordinate remediation with relevant teams (Sources, Technology, Business, Finance, etc.)
- Determine business impact level for the data quality issues
- Quantify materiality for proper escalations
- Customize and enhance data quality dashboard based on stakeholder requests
- Perform daily, weekly and monthly reviews and analyses of current processes using operational metrics and reports
- Create informative, actionable and repeatable reporting that highlights relevant business trends and opportunities for improvement
- Measuring and reporting to management on the progress of data quality improvement
- Conduct insightful, ad hoc analyses to investigate ongoing or one-time data issues
- Identify areas of business process improvement and make recommendations for long-term solutions
- Design interactive data quality dashboards
- Drive automation of processes
The staff member will be expected to cultivate relationships with staff or contractors in: Technology, Data Strategy, Data Operations, Finance and Corporate Treasury to ensure efficient identification, collection, and use of data along with the management of the associated data governance processes.
- Bachelor’s degree or the equivalent combination of education and experience is required.
- Degree in math, engineering, statistics, computational finance or economics preferred.
- Work toward or completion of MBA, CFA, or CPA/CA , FRM preferred.
- Five to seven years of total work experience preferred.
- Experience with finance data set and process: Balance Sheet, P&L, Consolidations, Client Product Profitability, liquidity analytics and reporting, interest rate risk management, asset-liability management, regulatory reporting, is strongly preferred.
- Knowledge of the structuring of data environments and applications that use big data processes is a preferred.
- Some experience with Business Intelligence data management and visualization applications specific to Hadoop big data and the ability to use SQL/Hive and other computer languages used in big data for analysis and structuring is preferred.
- Experience with issue management systems
- Competency with some leading data Application like AXIOM, ASG, Collibra, Informatica and profiling tools such as SQL, Power BI, Alteryx etc would preferred.
- Technical Skills with Python, SQL would be preferred
BNY Mellon is an Equal Employment Opportunity Employer.
Our ambition is to build the best global team – one that is representative and inclusive of the diverse talent, clients and communities we work with and serve – and to empower our team to do their best work. We support wellbeing and a balanced life, and offer a range of family-friendly, inclusive employment policies and employee forums.
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