Baker Tilly is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability or protected veteran status, gender identity, sexual orientation, or any other legally protected basis, in accordance with applicable federal, state or local law.
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Job Description
Baker Tilly is seeking a Senior Data Engineer to join the founding team of the firm's AI Product Group (APG). This hands-on engineering role is responsible for building scalable, secure, and reliable data infrastructure that powers the firm's AI applications and intelligent agents. The position focuses on two critical areas: developing a governed data foundation that transforms enterprise information and client documents into high-quality, traceable data products, and building the knowledge layer that enables AI agents to retrieve, interpret, and use information accurately. This includes developing production data pipelines, implementing data models and governance controls, and supporting advanced capabilities such as knowledge graphs, enterprise search, and retrieval.
As an early member of the AI Product Group, this individual will collaborate closely with data platform leadership, AI engineers, enterprise architects, business practitioners, and globally distributed technical teams to deliver production-ready data solutions. The role emphasizes strong data engineering fundamentals, hands-on development, data quality, and evaluation-driven engineering practices. The ideal candidate brings demonstrated experience building and operating modern lakehouse pipelines, strong Python and SQL skills, and the ability to develop reliable, maintainable solutions in a fast-paced environment. Prior experience with AI, machine learning, or knowledge graphs is not required; success in this role depends on sound engineering judgment, analytical thinking, and the ability to learn and apply emerging technologies.
Key Responsibilities
Design and Build Data Pipelines: Develop, implement, and maintain scalable batch and incremental data pipelines that transform structured and unstructured information into clean, validated, business-ready datasets within a modern lakehouse environment.
Develop and Maintain Lakehouse Architecture: Build and optimize data processing and transformation layers using modern lakehouse principles, including medallion architecture, data modeling, and integration of enterprise and legacy data sources.
Ensure Data Quality, Lineage, and Provenance: Implement automated validation, reconciliation, lineage tracking, and metadata management to ensure data accuracy, consistency, traceability, and reliability throughout the data lifecycle.
Build Data Models and Identity Resolution Capabilities: Develop scalable data models, data contracts, and entity-resolution processes that establish consistent client, engagement, and document identities across enterprise data sources.
Implement Data Governance and Access Controls: Support data cataloging, classification, permissions, and access management to ensure sensitive information is appropriately governed and accessible only to authorized users and AI agents.
Develop Knowledge Graph and Retrieval Capabilities: Build and maintain knowledge graph structures, data relationships, search indexes, and retrieval pipelines that enable AI applications to access relevant, accurate, and properly governed enterprise information.
Support AI Data Readiness and Continuous Learning: Develop pipelines that capture AI agent activity, practitioner feedback, reviews, and corrections to support ground-truth datasets, AI evaluation, and ongoing improvements in data and retrieval quality.
Drive Evaluation-Based Data Engineering: Establish measurable quality standards, testing frameworks, benchmarks, and validation processes to assess data extraction, retrieval effectiveness, and grounding accuracy.
Maintain Data Freshness and Reliability: Implement incremental processing, change data capture, versioning, and monitoring capabilities to ensure data products, knowledge graphs, and search indexes remain accurate and current as source systems evolve.
Own Production Delivery and Operational Support: Deploy, monitor, troubleshoot, and maintain production data pipelines, applying strong engineering practices, automated testing, code reviews, and operational documentation.
Support Knowledge Transfer and Technical Ownership: Collaborate with external engineering partners to transition data solutions into internally owned, documented, and maintainable production environments, including the development of reusable code, tests, and runbooks.
Collaborate Across Global and Cross-Functional Teams: Partner with AI Engineering, Platform & Production Engineering, enterprise data teams, subject matter experts, and engineering teams in India to align technical solutions with enterprise standards and business objectives.
Support Strategic Initiatives: Participate in special projects and perform other duties as assigned to advance the firm's AI and data platform capabilities.
Qualifications
Bachelor's degree in Computer Science, Engineering, or a related field; advanced degree preferred.
3 years of hands-on data engineering or software engineering experience, including demonstrated success developing, deploying, and maintaining production data pipelines.
Required experience independently building and operating production pipelines on a modern lakehouse platform, with demonstrated ownership of data processing, transformation, and operational reliability.
Required experience implementing data quality, lineage, or data contracts within production pipelines, including automated testing, reconciliation, validation, or traceability practices.
Strong proficiency in Python and SQL, with demonstrated ability to develop, optimize, test, and maintain production-quality data engineering solutions.
Strong understanding of data modeling principles, including the ability to design scalable, maintainable data structures that accommodate evolving business requirements and source systems.
Experience designing and developing batch and incremental data pipelines for structured and unstructured enterprise data.
Strong software engineering fundamentals, including version control, automated testing, code reviews, debugging, and development of reliable, repeatable data processing workflows.
Demonstrated ability to validate technical solutions through measurable outcomes, including automated tests, benchmarks, experiments, performance metrics, or comparative evaluations.
Experience with modern lakehouse technologies such as Databricks, Apache Spark, Delta Lake, or medallion architecture strongly preferred.
Experience with cloud-based data platforms, preferably Microsoft Azure, and associated data storage, processing, and integration services preferred.
Familiarity with enterprise data governance capabilities, including data catalogs, classification, access controls, lineage, and technologies such as Unity Catalog or comparable solutions preferred.
Experience with knowledge graphs, graph databases, graph data modeling, vector search, hybrid search, or retrieval-based applications preferred.
Familiarity with unstructured data processing, document extraction, metadata management, content chunking, or search indexing preferred.
Experience with entity resolution, master data management, change data capture, or incremental data processing preferred.
Understanding of data provenance, traceability, and governance requirements within complex enterprise or regulated environments.
Strong analytical thinking, technical judgment, and problem-solving skills, with the ability to independently evaluate trade-offs, troubleshoot complex issues, and develop scalable solutions.
Excellent written and verbal communication skills, including the ability to document technical solutions, explain complex concepts, and collaborate effectively with technical and nontechnical stakeholders.
Demonstrated ability to work with business subject matter experts, engineering teams, and globally distributed stakeholders to translate requirements into reliable data solutions.
Ability to manage competing priorities, adapt quickly to evolving requirements, and deliver high-quality solutions in a fast-paced, collaborative environment.
Experience using AI-assisted and agentic development tools to improve engineering productivity, or a strong willingness to incorporate these technologies into daily development practices.
Prior experience with AI, machine learning, LLM systems, or knowledge graphs is not required; demonstrated engineering fundamentals and the ability to learn emerging technologies are essential.
No professional certifications or licenses required.
The pay rate range for this job position are listed below. Actual compensation is influenced by a variety of relevant factors including but not limited to applicant’s skills, prior experience, qualifications, degrees, professional certifications, work arrangements and geographic location. Baker Tilly offers a comprehensive compensation and benefits package to eligible employees.
The national pay rate range is $185,000 –$230,000
In California: pay rate range is $185,000 –$230,000
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