Lead Data Engineer

Baker Street Advisors LLC (BSA) is a preeminent, private wealth management firm, based in San Francisco. We manage over $21.1 billion (AUM) in aggregate wealth for 500+ families. We are a team of dedicated professionals who put our clients’ interests first and take pride in making a positive impact on their lives. Baker Street Advisors is seeking a Lead Data Engineer to design, build, and maintain the firm’s next-generation data infrastructure in support of our investment, reporting, and knowledge-management platforms. This individual will play a central and collaborative role in leading the IBOR/Reporting transition and the related integration and build out of core firm data models; building a normalized and governed internal data environment; and structuring data so it can be effectively accessed, queried, and delivered through AI-enabled systems such as Glean. The role will sit within the Investment Strategy Group (ISG) and work closely with the Business Process Engineer, the Head of Operations, and key business stakeholders. It will be responsible for building durable, well-governed data pipelines, models, and query-ready structures that power decision-making, operations, reporting, and knowledge delivery across the firm. This position is designed for a candidate who can simultaneously architect and implement scalable data solutions; translate business and investment needs into clear data requirements and models; and collaborate across functions to modernize processes, workflows, and information access. A successful candidate will combine strong data engineering skills with an understanding of investment and wealth-management workflows, excellent communication abilities, and a bias toward creating durable, well-documented systems rather than one-off solutions. They should also be comfortable designing data environments that support high-quality querying into LLM-based tools and creating Skills that route recurring questions to trusted data and logic. Primary Responsibilities IBOR / Reporting Transition & Integration Lead the design, implementation, and migration of the firm’s data architecture to support the new IBOR/Reporting environment. Define and maintain the core data models and entity mappings that connect reporting, CRM, operational, and investment systems so that key records are consistent across the firm. Design integration patterns and transformation logic that align data definitions across Practifi and other internal and external systems, with particular focus on the integrity of shared data models. Partner with Operations, Client Service, and business stakeholders to ensure that the new reporting stack meets requirements for timeliness, accuracy, auditability, and downstream usability. Establish monitoring, quality checks, reconciliation processes, and exception handling to support reliable production reporting and a controlled transition from legacy data flows. Data Lake, Internal Data Normalization, and Governance Own the design and supervise partner implementation of the firm’s long-term data lake environment and the supporting governance processes around it. Organize investment, client, operational, and knowledge data into a coherent, normalized, and well-documented ecosystem that can be trusted across reporting, analytics, and knowledge workflows. Design and manage centralized data models, identifiers, hierarchies, and classifications so that data can be reconciled and reused consistently across systems and use cases. Implement data quality frameworks, lineage standards, access controls, and documentation practices that maintain a high degree of trust in firm data. Create reusable, governed data products and interfaces that support reporting, analysis, oversight, and the consistent reuse of high-quality information across the organization. Work with internal stakeholders to optimize data completeness, latency, and usability while ensuring the environment remains practical, scalable, and supportable. AI-Ready Data Environment, Firm Knowledge & Skills Enablement Create the data structures, metadata, and access patterns needed for AI tools to reliably query and deliver high-quality information, beginning with Glean. Structure both structured and unstructured firm information so that standard inquiries can be answered against trusted data and governed logic rather than ad hoc manual interpretation. Collaborate with the Business Process Engineer and subject matter experts to define and build Skills that route recurring questions to the right data sources, business rules, and presentation formats. Design and maintain query-ready models and semantic context that improve the relevance, consistency, and explain-ability of information delivered through LLM-based tools. Prioritize data engineering work that improves retrieval quality, response accuracy, and presentation creation for internal knowledge and decision-support use cases. Support the creation of repeatable inquiry patterns, presentation frameworks, and knowledge artifacts that are grounded in current, high-quality firm data. Collaboration with Business Process Engineer & Process Redesign Partner closely with the Business Process Engineer to ensure that new and existing processes are data-informed, automatable, and measurable. Translate process maps and business requirements into concrete data structures, field definitions, integration points, and query logic. Help redesign workflows so that information can move more consistently from source systems into reporting, operational, and AI-enabled consumption layers. Participate in and at times lead working sessions with stakeholders to prioritize data-driven process improvements and socialize changes. Document and communicate the impact of process changes on data quality, information access, reporting outputs, and downstream analytical workflows. Develop internal documentation and practical interfaces that allow non-technical users to understand, access, and rely on key data assets. Contribute to the continuous improvement of the firm’s technology and data stack, with emphasis on interoperability, maintainability, query-ability, and fitness for evolving AI-enabled workflows. Qualifications Experience 5–10 years of relevant experience in data engineering, data platform development, data pipeline development, or related roles in financial services, fintech, or adjacent industries. Direct experience architecting and maintaining production data environments that support reporting, analytics, operational workflows, or enterprise information access. Experience aligning data models across multiple business systems and translating business requirements into durable integration logic strongly preferred. Exposure to or experience with investment, wealth management, or financial data strongly preferred. Experience designing data environments that support search, semantic retrieval, AI-enabled querying, or knowledge systems is a meaningful plus. Experience working in environments where data quality, lineage, and auditability are critical. Education Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical field required. An advanced degree in a relevant discipline is a plus but not required. Additional coursework or certifications in data engineering, information architecture, analytics, or related fields are additive. Skills & Attributes Technical Skills Strong proficiency in SQL and at least one modern programming language used for data engineering, such as Python. Hands-on experience with data modeling, data transformation, pipeline design, and the movement of data across systems and use cases. Ability to design data structures and metadata that support efficient querying, reliable retrieval, and effective delivery of information through LLM-based tools. Experience creating governed datasets, semantic context, or similar structures that improve the consistency of responses to recurring business questions. Ability to design and implement robust data validation, monitoring, and observability around critical data flows. Comfort working with APIs, flat files, and third-party data feeds, including error handling, reconciliation, and controlled integration into internal data models. Understanding of security, privacy, and access-control best practices in a data context. Domain & Analytical Skills Solid understanding of how investment and client data flow through portfolio accounting, reporting, CRM, operations, and knowledge systems, or demonstrated ability to learn this quickly. Ability to think in terms of entities, relationships, business rules, and process flows, and translate those into clean, maintainable data models. Ability to move from a business question to the underlying data structure, retrieval logic, and presentation requirements needed to answer it well. Appreciation for after-tax, client-centric investment thinking and how data supports that philosophy, even if not in a core investment role. Ability to translate technical concepts into clear, accessible language for non-technical stakeholders. Strong organizational skills with the ability to manage multiple initiatives and deadlines while maintaining attention to detail. Demonstrated ability to work collaboratively with investment professionals, operations, client service, and technology partners. Comfortable leading working sessions, walkthroughs, and training for both technical and non-technical audiences. Personal Attributes Intellectually curious and motivated to improve systems and processes, not just maintain them. High degree of ownership and accountability for the reliability and quality of the data stack. Pragmatic and solution-oriented, with a bias for building simple, durable architectures over overly complex designs. Demonstrated professionalism, integrity, and alignment with Baker Street’s values, culture, and client-first mindset. Ideal Candidate Profile The ideal candidate is a builder who enjoys working at the intersection of data, systems, business processes, and information delivery. They are energized by the challenge of modernizing a firm’s data infrastructure in a way that strengthens reporting, improves the alignment of core data models across systems, and creates a governed environment for answering important questions quickly and accurately. They take pride in well-structured data models, clean pipelines, and clear documentation, and they understand that the value of a data environment is not only in storing data but in making it reliably retrievable, explainable, and useful. They are comfortable designing systems that support recurring inquiry through LLM-based tools and building Skills that connect standard questions to trusted data and logic. They are comfortable in both greenfield and legacy environments and can thoughtfully balance short-term business needs with a long-term architectural vision. Salary Range: $185,000 - $235,000 plus an annual discretionary bonus. Benefits include: medical, dental, vision, life insurance, 401(k) plan participation and an Unlimited PTO policy. 401k and Roth 401k Retirement Plans Health Savings Account with Employer Contributions Flexible Spending Plans for Health Care and Dependent Care Medical, Dental, Vision, and Life Insurance Short- & Long-Term Disability Insurance Group Paid Life and AD&D Insurance Professional Education Reimbursement MBA Degree Reimbursement Family Leave Benefits Unlimited Paid Time Off #J-18808-Ljbffr Jobleads-US