Sr Data Engineer

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Baker Tilly is seeking a Sr Data Engineer to join the founding team of the firms AI Product Group (APG). This hands-on engineering role is responsible for building scalable secure and reliable data infrastructure that powers the firms 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 firms AI and data platform capabilities.

Qualifications

• Bachelors 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 applicants 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 $185000 $230000

In California: pay rate range is $185000 $230000

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