Senior Data Specialist
Required skills
Bachelor's degree or equivalent mix of education and professional experience, 7+ years of relevant experience in data engineering, analytics, or a closely related discipline, strong expertise in building and supporting scalable data pipelines, ETL/ELT workflows, and enterprise data integration, advanced proficiency in SQL and Python for data processing, performance tuning, and analytics support, solid knowledge of data modeling, data architecture, metadata management, and governance practices, experience applying data standardization, reliability, quality improvement, and stewardship practices, effective communication and leadership skills to influence technical direction and mentor team members.
Preferred skills
Master's degree, R, hands-on experience with modern platforms and tools such as Databricks, Snowflake, Azure Data Factory, and PySpark, familiarity with big data and distributed technologies such as Hadoop, Kafka, and distributed file systems, experience building custom components, analytics applications, and solutions enabling advanced analytics or AI use cases, familiarity with cloud environments and architectures, especially Azure, including SaaS, PaaS, and IaaS concepts, experience in healthcare or other regulated industries.
Technologies
AI, Azure, Big Data, Cloud, Databricks, ETL, Hadoop, IaaS, Kafka, PaaS, Python, PySpark, SQL, Security, Snowflake.
Responsibilities
Design and deliver scalable data pipelines and ETL/ELT programs that connect complex internal and external data sources for batch and real-time processing, write and refine advanced SQL and Python solutions to improve data handling, performance, and analytical results, lead data discovery, requirements gathering, and source identification for analytical and operational initiatives, build and maintain metadata repositories, including definitions, lineage, and business rules, to support data integrity and usability, create and sustain processes that improve data standardization, reliability, quality, and governance compliance, troubleshoot and resolve complex data and analytics issues across production and development environments, including tuning databases and pipelines, develop reusable query libraries and custom software components that support analytics, reporting, and AI/ML solutions, assess and implement modern tools, technologies, and best practices to strengthen data engineering capabilities and platform performance, ensure compliance with governance, security, and regulatory requirements through robust validation, quality checks, and documentation, support testing, monitoring, and validation of data pipelines by creating test cases and verification checks, collaborate with architects, analysts, data scientists, and governance partners to align solutions with enterprise standards and business priorities, provide technical leadership, help shape design decisions, and mentor junior engineers to promote best practices and continuous improvement.
Seniority
Senior.
Domain
Healthcare.