AI Data Engineer

IntegriChain • Philadelphia, PA

8h • Hybrid

About The Position

IntecliChain is the data and application backbone for market access departments of Life Sciences manufacturers. We deliver the data, the applications, and the business process infrastructure for patient access and therapy commercialization. More than 250 manufacturers rely on our ICyte Platform to orchestrate their commercial and government payer contracting, patient services, and distribution channels. ICyte is the first and only platform that unites the financial, operational, and commercial data sets required to support therapy access in the era of specialty and precision medicine. With ICyte, Life Sciences innovators can digitalize their market access operations, freeing up resources to focus on more data-driven decision support. With ICyte, Life Sciences innovators are digitalizing labor-intensive processes – freeing up their best talent to identify and resolve coverage and availability hurdles and to manage pricing and forecasting complexity.
We are headquartered in Philadelphia, PA (USA), with offices in: Ambler, PA (USA); Pune, India; and Medellín, Colombia. For more information, visit www.integrichain.com, or follow us on Twitter @IntegriChain and LinkedIn.
This role offers flexibility, but candidates must reside in Pennsylvania, New Jersey, or New York and be within a reasonable travel distance of our Philadelphia office, as regular in-person collaboration is required.
Join the Data Science team as an AI Data Engineer responsible for building the data foundations that make enterprise AI products accurate, explainable, and scalable. This role will design and implement Snowflake and dbt pipelines from raw source data to curated gold-layer datasets, create semantic models that LLM tools can use reliably, and partner with data science, product, and engineering teams to convert data dictionaries and business definitions into AI-ready data products. The ideal candidate is a strong data engineer with deep Snowflake/dbt experience and a practical understanding of how semantic layers, ER relationships, denormalized models, and metadata quality influence LLM and agent performance.

Requirements

• 6+ years of experience in data engineering, analytics engineering, database engineering, or data platform development in production environments.
• Strong hands-on experience with Snowflake, including SQL development, performance tuning, security-aware design, cost optimization, and large-volume processing.
• Strong hands-on experience with dbt or comparable ELT tooling, including models, tests, documentation, lineage, and environment promotion.
• Experience building raw-to-curated-to-gold data pipelines and business-ready datasets.
• Strong SQL and Snowflake development skills, including complex transformations, views, stored procedures/Snowflake Scripting, and query optimization.
• Experience creating semantic layers, semantic models, metrics, dimensions, relationships, and curated analytical views.
• Good understanding of ER modeling, dimensional modeling, denormalized consumption models, and data grain management.
• Experience translating data dictionaries and business definitions into physical models, dbt models, and semantic-layer definitions.
• Understanding of Snowflake Cortex capabilities such as Cortex Analyst, Cortex Complete, and semantic-model-driven natural-language querying.
• Ability to partner with data science, product, engineering, and business teams to deliver AI-ready data products.

Nice To Haves

• Experience in life sciences, healthcare, pharma commercialization, MDM, patient data, channel data, or commercial data platforms.
• Experience with Snowflake semantic views, Cortex Analyst, Cortex Search, or other AI/LLM data platform capabilities.
• Experience with data quality frameworks, metadata management, data observability, and lineage tooling.
• Experience with orchestration tools such as dbt Cloud jobs, Airflow, Dagster, cloud-native schedulers, or similar platforms.
• Experience with Python for data automation, metadata processing, testing, or API integrations.
• Experience designing governed data products for BI, AI/ML, natural-language analytics, or agentic applications.
• Snowflake SnowPro, dbt certification, or equivalent data engineering credentials.

Responsibilities

• Build reliable data pipelines from raw source data through curated silver layers and business-ready gold layers using Snowflake and dbt.
• Develop modular dbt models, tests, documentation, exposures, and lineage-friendly transformation patterns.
• Implement incremental processing, snapshots, audit columns, reconciliation, data quality checks, and restartable pipeline patterns.
• Optimize Snowflake SQL and dbt workloads for performance, scalability, cost, and maintainability.
• Work with orchestration and DevOps/SRE teams to support CI/CD, environment promotion, pipeline monitoring, and operational runbooks.
• Create Snowflake semantic models and curated views that support accurate natural-language querying through Snowflake Cortex Analyst and related LLM tools.
• Translate approved data dictionaries into semantic model dimensions, facts, metrics, synonyms, descriptions, relationships, and business rules.
• Design ER relationships and join paths that are explicit, accurate, and easy for semantic-layer tools and AI agents to use.
• Create denormalized or consumption-optimized models where appropriate to reduce ambiguity and improve LLM answer quality.
• Partner with AI developers to understand tool schema needs, agent workflows, and how data model design affects LLM tool performance.
• Design logical and physical models that support enterprise data consolidation, analytical reporting, AI workflows, and business operations.
• Work across source systems, files, APIs, cloud storage, operational systems, and analytical platforms to integrate data into Snowflake.
• Create reusable patterns for source-to-target mapping, schema evolution, master/reference data alignment, and data product publishing.
• Collaborate with business and technical stakeholders to validate data definitions, grain, relationships, hierarchies, and measures.
• Support data consolidation across Integrichain by rationalizing overlapping datasets and aligning enterprise definitions.
• Understand Snowflake Cortex capabilities, including Cortex Analyst, Cortex Complete, semantic views/models, and metadata-driven AI workflows.
• Prepare data models and semantic layers for accurate LLM usage, including clear naming, descriptions, relationships, metrics, and governance metadata.
• Support AI Explorer and similar applications by ensuring curated datasets are reliable, performant, explainable, and governed.
• Partner with AI and application teams to troubleshoot semantic model issues, poor AI answers, ambiguous joins, missing metadata, or incorrect measures.
• Contribute to standards for AI-ready data design, semantic model review, data dictionary alignment, and LLM-friendly data modeling.

Benefits

• Excellent and affordable medical benefits
• Student Loan Reimbursement
• Flexible Paid Time Off
• Paid Parental Leave
• 401(k) Plan with a Company Match
• Robust Learning & Development opportunities including over 700+ development courses free to all employees

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed