Strategic Data Provisioning Specialist

Data Analytics

We are seeking an execution-focused Strategic Data Provisioning Specialist to deliver on four primary service areas: provisioning new and differentiated data, tracing and uplifting lineage, resolving data quality issues, and uplifting existing data. This role requires a unique combination of deep technical expertise, strategic thinking, and collaborative leadership to make data available for AI/analytics, provide transparency into data flows, embed preventative controls, and enrich metadata to accelerate adoption.

Job Responsibilities

1. Provision New/Different Data

·                     Make data available for AI and analytics initiatives, working closely with use case owners to define requirements and manage product dependencies. Provide transparency and visibility into bottlenecks and progress in making AI-ready data available for innovation

·                     Collaborate with business, technology, and operations partners to understand data requests and accelerate provisioning through deployment of "AI for Data". Drive executive visibility of progress in making critical data sources available, including performance metrics and adoption tracking

·                     Support agile product routines to oversee cross-product data dependencies and prioritize delivery

2. Trace & Uplift Lineage

·                     Identify the lineage and provenance of critical data assets to support governance, regulatory, and business requirements. Embed evergreen controls on data flows to improve safety and meet regulatory requirements

·                     Develop and deliver data lineage analysis and documentation that provides executive visibility on progress meeting critical SLAs (including blockers, resourcing, etc.)

·                    Uplift data flows for critical data to include controls, transparency, and traceability. Drive insight into areas of efficiency and risk through consolidation and reengineering of data flows

3. Resolve Data Quality Issues

·                     Lead data quality issue root cause analysis using deep data profiling and advanced analytics techniques, fix the cause of identified data quality issues and embed uplifted evergreen controls on data flows to prevent future failures

·                     Develop proactive controls to reduce the time from data quality issue identification to resolution, improving client experience, drive operational efficiency through elimination of cost of poor quality (COPQ) and demonstrate control environment improvements and reduction in toil to achieve benefits through common tooling and frameworks

4. Uplift Existing Data

·                     Uplift the metadata (semantic layer) of existing data to make it more valuable to users and AI applications (AKA "Brownfield" data enrichment)

·                     Support AI and Natural Language Query (NLQ) usage through enhanced data cataloging and discoverability. Accelerate adoption of Mesh data architecture by enriching existing data assets with improved metadata, data quality scores, and lineage information

·                     Reduce consumer friction due to poor data catalog quality and incomplete documentation. Develop and deliver data product prototypes that demonstrate the value of uplifted data assets

Required Qualifications, Capabilities, and Skills

·                     7+ years of experience in data science, analytics, data engineering, or data management within financial services

·                     Deep subject matter expertise in wealth and asset management, covering customer, account, position, transaction, and/or reference data domains

·                     Proven execution ability in a matrixed and complex environment with the ability to influence people at all levels of the organization

·                     Experience in strategic or transformational change initiatives, including data governance, data quality, or analytics transformation programs

·                     Strong technical skills in data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, cloud platforms)

·                     Understanding of data lineage concepts and experience with lineage analysis, metadata management, and data cataloging

·                     Excellent communication skills with the ability to convey complex technical concepts to diverse audiences including executive leadership

·                     Ability to work in a highly collaborative and intellectually challenging environment, willingness to challenge the status quo, think creatively, problem-solve, and drive innovation

·                     Experience with data quality frameworks, including profiling, rule development, issue remediation, and preventative controls.

Exposure to Cloud Platforms like AWS, Azure, GCP, data tools like Data profiling tools, data quality platforms, metadata management systems, visualization tool like Tableau, Power BI, or similar BI tools, methodologies like Agile/Scrum, DevOps, DataOps, Version control like Git, GitHub, Bitbucket

Preferred Qualifications, Capabilities, and Skills

·                     Strong proficiency in data science and analytics tools: Python, R, SQL, Spark, and cloud data platforms (AWS, Azure, GCP). Experience with data visualization and reporting tools (e.g., Tableau, Power BI) to deliver executive dashboards and performance metrics

·                     Hands-on experience with data lineage tools and techniques, including graph databases and metadata management platforms

·                     Knowledge of data governance frameworks, data quality dimensions, and regulatory requirements (e.g., BCBS 239, GDPR)

·                     Experience with AI/ML technologies and their application to data management challenges (e.g., automated data profiling, metadata enrichment)

·                     Understanding of agile and product management methodologies and experience working in agile teams

·                     Ability to multi-task in a fast-paced environment and operate independently with minimal supervision. Experience building and growing capabilities and developing talent in data science or data management teams

·                     Strong judgment with the ability to balance strategic vision with pragmatic, incremental delivery. Excellent interpersonal skills and ability to build strong working relationships with business, technology, and control stakeholders across global teams