AVP Data Product Analyst

Where Ambition Meets Innovation

Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you'll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.

As AVP, Data Product Analyst, you will have the opportunity to shape LPL's strategic data foundation for the future. In this role, you will connect Business product owners with Technology delivery teams using data to power applications and experiences for our Advisors and Investors. You will be accountable for multiple domains of data that power products including Advisor and Investor platforms, performance reporting, supervision, account lifecycle management, billing, compensation, trading, money movement, proposal generation, CRM, BI reporting and institutional data distribution, etc.

• Author both operational and analytical data quality rules for respective data and business domains. Document the rules and partner with technology teams and engineers to have the rules built into data pipelines, APIs, a commercial tool quality framework, etc.
• Develop data profiling, quality, and alerting capabilities leveraging database and ETL capabilities (DBT, Airflow, SQL, etc.) or commercial data management tools (Collibra, Alation, atlan, AWS Datazone, data.world, etc.)
• Develop and document standardized processes and workflows for remediating data issues by working with Relationship Management, Servicing, and Operations teams. Assist with data issue research, impact analysis, and fixes by partnering with support and technology teams.
• Translate business product, application and capability requirements for Advisors and Investors into data needs and solutions. These include business logic and transformations specs for data pipelines, inputs and business context for data modeling and logic embedded in consuming APIs.
• Utilize homegrown solutions, commercial data governance tools, or Excel, to build and document data lineage views of data transformations across the data ecosystems from source to target. Maintain data lineage artifacts and leverage them for data research and remediation efforts. Work with enterprise data architects to document data flows in conceptual and reference architecture diagrams for accountable data and business domains.
• Use either commercial governance tools or manually capture, document, and maintain data dictionaries at the attribute level for accountable domains. Partner with the enterprise data modelling team to ensure consistency of data definitions, classifications, taxonomies, hierarchies, etc. across data assets and consuming applications.
• Work with data product peers to identify inconsistencies in data to standardize on data definitions, code sets, naming conventions, classifications, consumption contracts, and taxonomies.
• Define and configure workflows, metadata structures, data quality rules, and classification schemes based on defined requirements.
• Author and enter Feature and Story-level work in Jira for technology teams to execute against.
• Work with data domain teams and the enterprise modeling team to understand both the domain-level data models and the enterprise data model to determine if fit for purpose for new project intake or if changes are required for the data domain. Provide scope and level of complexity if development work needed. Document drift and gaps between the enterprise logical data model and domain-level physical data models.
• Represent data and participate in QE and end-to-end testing of data products.
• Populate and enrich the business glossary or data dictionary and supporting and resolving data quality issues.
• Leverage software applications, and tooling to construct, monitor, govern and maintain new data products for the company. Data products will be developed on both the producing and consuming sides of the organization.
• Manage the data sharing and data delivery agreements as defined by the Data Design Authority, business stakeholders, data modeling and engineering teams.
• Accountable for partnering with engineering to implementation and execution of the data quality and privacy rules.
• Ensure alignment of data schemas, models, contracts, and templates from ingestion to the enterprise data model to systems of record domains to consumption layers. Align and govern physical models to logical data models.
• Partner with the Risk organization for data archiving, retention, privacy, and data destruction policies and requirements for critical data elements.
• Business metadata management across accountable data domains and data products.
• Onboard, govern and certify new CDEs introduced into the data ecosystem.
• Perform technical traceability mapping and data lineage tracking.
• Validate and certify new data sources.
• Partner with data domain (SOR) teams and business stakeholders to author and implement data quality rules and…