Sr Data Engineer

Baker Tilly•San Diego, CA

2d•$185,000 - $230,000

About The Position

Baker Tilly is hiring a Sr Data Engineer for the founding team of its AI Product Group. This hands-on engineering role builds scalable, secure, and reliable data infrastructure to support the firm's AI applications and intelligent agents. The work centers on creating a governed data foundation that turns enterprise information and client documents into high-quality, traceable data products, and developing a knowledge layer that helps AI agents retrieve, interpret, and use information accurately. The role includes building production data pipelines, implementing data models and governance controls, and supporting knowledge graphs, enterprise search, and retrieval capabilities. As an early member of the AI Product Group, the engineer will work with data platform leadership, AI engineers, enterprise architects, business practitioners, and globally distributed technical teams. The role emphasizes data engineering fundamentals, hands-on development, data quality, and evaluation-driven engineering. The successful candidate will have 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 AI, machine learning, or knowledge graph experience is not required.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field.
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, including 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, including developing, optimizing, testing, and maintaining production-quality data engineering solutions.
Strong understanding of data modeling principles and the ability to design scalable, maintainable data structures for 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 independently evaluating trade-offs and troubleshooting complex issues.
Excellent written and verbal communication skills, including documenting technical solutions and explaining complex concepts to 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.
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 such as data catalogs, classification, access controls, and lineage, including 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.

Responsibilities

Develop, implement, and maintain scalable batch and incremental data pipelines that transform structured and unstructured information into clean, validated, business-ready datasets.
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 for sensitive information 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 quality standards, testing frameworks, benchmarks, and validation processes for 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.