01Responsibilities
Integrate enterprise AI tools and AI-enabled capabilities into data engineering workflows, setting clear development goals and actively removing barriers to accelerate team adoptionChampion the ethical and responsible use of AI across data platforms by embedding transparency, fairness, data privacy, and accountability throughout the entire data and AI lifecycleStrategically leverage AI and advanced analytics to solve complex health and pharmacy data challenges, unlock operational opportunities, and deliver tangible business valueChampion AI as a core driver of team success, proactively shaping how AI-driven solutions are developed, deployed, and utilized across reporting and analytics domainsArchitect, design, and maintain scalable data pipelines, ETL/ELT workflows, and integration solutions across Azure and Snowflake environmentsDefine data modeling standards (dimensional, relational, NoSQL) and optimize enterprise data platforms using Snowflake, Microsoft Fabric, and Azure data servicesProvide technical leadership and mentorship to data engineers, managing a team of 5+ resources while enforcing coding, testing, and CI/CD best practicesBuild and optimize batch and real-time streaming pipelines across APIs, databases, and streams using Kafka, Event Hubs, Airflow, Spark, and DatabricksImplement data validation, lineage, governance frameworks, and metadata management (e.g., Azure Purview) while ensuring compliance with HIPAA, GDPR, and security standardsPartner with business, BI, analytics, and data science teams to translate complex pharmacy requirements into performant, reusable technical designsComply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do soRequired Qualifications:
10+ years of experience in Data Engineering or Data Science, including 3+ years in a technical leadership role managing a team of at least 5 resources8+ years of hands-on experience in Data Engineering focused on ETL/ELT development using SQL, Databricks, Python, and Spark/PySpark5+ years of experience in data modeling, query performance tuning, data streams, and secure data sharing5+ years of experience within cloud ecosystems, specifically Azure (ADF, Synapse, ADLS, Purview) and Snowflake3+ years of experience writing complex SQL queries/stored procedures for reporting/extracts and working with PowerBI and analytics platforms2+ years of experience in DevOps practices including CI/CD pipelines, Git, Terraform, and containerization (Docker/Kubernetes)
Preferred Qualifications:
Bachelor's degree or 4+ years of equivalent technology experienceDemonstrated experience driving AI adoption, setting technology strategy, and establishing governance/ethical AI practices within data engineering teamsProven experience collaborating with Analytics, BI, Data Science, and Product teams to deliver trusted, reusable, and performant pharmacy data assetsHands-on experience developing streaming and orchestration pipelines using Kafka, Event Hubs, Apache Airflow, or Microsoft FabricIn-depth understanding of Lakehouse/data lake architectures, NoSQL systems, metadata management, and compliance .
