Sr Data Engineer

Baker Tilly•San Jose, CA

11d

About The Position

Baker Tilly is seeking a Sr Data Engineer to join the founding team of its AI Product Group (APG). This hands-on engineering role builds scalable, secure, and reliable data infrastructure for the firm’s AI applications and intelligent agents. It focuses on creating a governed data foundation that transforms enterprise information and client documents into high-quality, traceable data products, as well as building the knowledge layer that enables AI agents to retrieve, interpret, and use information accurately. The role develops production data pipelines, implements data models and governance controls, and supports capabilities such as knowledge graphs, enterprise search, and retrieval. As an early member of the AI Product Group, the engineer collaborates with data platform leadership, AI engineers, enterprise architects, business practitioners, and globally distributed technical teams to deliver production-ready data solutions. The role emphasizes sound data engineering, hands-on development, data quality, and evaluation-driven engineering practices. Prior AI, machine learning, or knowledge graph experience is not required; strong engineering judgment, analytical thinking, and adaptability are essential. The national pay rate range is $185,000–$230,000. The California pay rate range is also $185,000–$230,000. Actual compensation depends on relevant factors, including skills, experience, qualifications, degrees, certifications, work arrangements, and geographic location. Baker Tilly offers a comprehensive compensation and benefits package to eligible employees.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field; an advanced degree is preferred.
At least 3 years of hands-on data engineering or software engineering experience, including developing, deploying, and maintaining production data pipelines.
Experience independently building and operating production pipelines on a modern lakehouse platform, with ownership of data processing, transformation, and operational reliability.
Experience implementing data quality, lineage, or data contracts in production pipelines, including automated testing, reconciliation, validation, or traceability practices.
Strong proficiency in Python and SQL, with the ability to develop, optimize, test, and maintain production-quality data engineering solutions.
Strong understanding of data modeling and the ability to design scalable, maintainable data structures that accommodate evolving business requirements and source systems.
Experience designing and developing batch and incremental pipelines for structured and unstructured enterprise data.
Strong software engineering fundamentals, including version control, automated testing, code reviews, debugging, and reliable, repeatable data processing workflows.
Ability to validate technical solutions through measurable outcomes such as automated tests, benchmarks, experiments, performance metrics, or comparative evaluations.
Understanding of data provenance, traceability, and governance requirements in complex enterprise or regulated environments.
Strong analytical thinking, technical judgment, and problem-solving skills, including the ability to independently evaluate trade-offs, troubleshoot complex issues, and develop scalable solutions.
Excellent written and verbal communication skills, including documenting technical solutions, explaining complex concepts, and collaborating with technical and nontechnical stakeholders.
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 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 them into daily development practices.
No professional certifications or licenses are required.

Nice To Haves

Advanced degree preferred.
Experience with modern lakehouse technologies such as Databricks, Apache Spark, Delta Lake, or medallion architecture.
Experience with cloud-based data platforms, preferably Microsoft Azure, and associated data storage, processing, and integration services.
Familiarity with enterprise data governance capabilities, including data catalogs, classification, access controls, lineage, and technologies such as Unity Catalog or comparable solutions.
Experience with knowledge graphs, graph databases, graph data modeling, vector search, hybrid search, or retrieval-based applications.
Familiarity with unstructured data processing, document extraction, metadata management, content chunking, or search indexing.
Experience with entity resolution, master data management, change data capture, or incremental data processing.
Prior AI, machine learning, LLM systems, or knowledge graph experience is not required.

Responsibilities

Develop, implement, and maintain scalable batch and incremental pipelines that transform structured and unstructured information into clean, validated, business-ready datasets in a modern lakehouse environment.
Build and optimize data processing and transformation layers using lakehouse principles, including medallion architecture, data modeling, and integration of enterprise and legacy data sources.
Implement automated validation, reconciliation, lineage tracking, and metadata management to ensure data accuracy, consistency, traceability, and reliability.
Develop scalable data models, data contracts, and entity-resolution processes to establish consistent client, engagement, and document identities across enterprise data sources.
Support data cataloging, classification, permissions, and access management so sensitive information is governed and available only to authorized users and AI agents.
Build and maintain knowledge graph structures, data relationships, search indexes, and retrieval pipelines for AI applications.
Develop pipelines that capture AI agent activity, practitioner feedback, reviews, and corrections to support ground-truth datasets, AI evaluation, and improvements in data and retrieval quality.
Establish measurable quality standards, testing frameworks, benchmarks, and validation processes for assessing data extraction, retrieval effectiveness, and grounding accuracy.
Implement incremental processing, change data capture, versioning, and monitoring to keep data products, knowledge graphs, and search indexes accurate and current.
Deploy, monitor, troubleshoot, and maintain production data pipelines using automated testing, code reviews, and operational documentation.
Collaborate with external engineering partners to transition data solutions into internally owned, documented, maintainable production environments, including reusable code, tests, and runbooks.
Partner with AI Engineering, Platform & Production Engineering, enterprise data teams, subject matter experts, and engineering teams in India to align solutions with enterprise standards and business objectives.
Participate in special projects and perform other duties as assigned to advance the firm’s AI and data platform capabilities.