Sr Data Engineer

University of Maryland Medical System•North Bethesda, MD

12d

About The Position

Under limited direction, this role is responsible for the design, development, and implementation of core technologies supporting the organization’s data analysis and analytics infrastructure. As a key member of a high-performing team, the individual ensures data pipelines are reliable, scalable, and efficiently maintained, enabling the organization to derive meaningful insights from diverse and complex datasets.

Requirements

Bachelor’s degree in Computer Science, Mathematics, Information Systems, Engineering, Physical Sciences, Life Sciences, or a closely related field is required. Equivalent professional experience will also be considered.
Seven (7+) or more years of experience designing, implementing, and supporting systems within large-scale analytics or data engineering environments, including work with multiple disparate systems and data sources.
Strong proficiency in one or more programming or scripting languages (e.g., C/C++, Python, Ruby).
Experience working in Agile or other rapid application development environments.
Solid understanding of object-oriented design principles, coding standards, and testing methodologies, along with experience building and maintaining large-scale data platforms and software systems.
Strong understanding of data analysis, end-user needs, and business requirements, with the ability to translate these into effective technical solutions.
Advanced knowledge of statistics and/or applied mathematics.
Deep expertise in data modeling, with a clear understanding of various data structures and their appropriate use cases.
Working knowledge of relational, document-oriented, and object-oriented databases (e.g., PostgreSQL, Oracle, SQL, MongoDB).
Strong foundation in data mining, machine learning, and/or natural language processing.
Proven ability to clean, transform, and prepare complex datasets, as well as design and implement algorithms.
Experience with the Hadoop ecosystem (e.g., HDFS, Spark, MapReduce, Pig, Hive), as well as tools such as Ranger, Atlas, or Falcon.
Experience with messaging systems such as ActiveMQ or RabbitMQ.
Familiarity with big data machine learning frameworks (e.g., Mahout, Spark ML, TensorFlow).
Demonstrated ability to thrive in a high-performance, collaborative team environment.
Ability to architect and implement highly scalable, distributed systems using open and non-proprietary technologies.
Expert-level understanding of data modeling concepts, including the strengths and limitations of different data structures across various use cases.

Nice To Haves

Relevant certifications are a plus.

Responsibilities

Ensure analytics infrastructure and associated systems align with business requirements and industry best practices.
Collect, process, and transform raw data from multiple sources (e.g., APIs, scripts, SQL queries) into formats suitable for analysis.
Gather, analyze, and translate business and application requirements into effective data models.
Design and develop scalable data models, including relational and dimensional structures.
Enable batch and real-time analytical processing solutions using modern and emerging technologies.
Identify and recommend new data acquisition opportunities and innovative uses of existing data.
Develop, test, and document data systems to build robust, scalable analytics applications.
Translate complex functional and technical requirements into detailed architecture and high-performance system designs.
Architect and implement next-generation big data analytics frameworks.
Expand and enhance data platform capabilities to address evolving data challenges.
Create and maintain data flow diagrams to support business systems.
Build automation tools and ensure data integrity through aligned data availability and integration processes.
Conduct technology and product research to refine requirements, resolve issues, and strengthen the technology stack.
Contribute to the design and evolution of enterprise-wide data architecture and documentation standards.
Promote and support standardization of documentation, data practices, and application development standards.
Develop and maintain data dictionaries, metadata, and comprehensive data models.
Establish and manage datasets and processes for data mining and production environments.
Collaborate with data scientists and engineers to design and develop high-performance algorithms, predictive models, and prototypes.
Partner with development teams to integrate advanced analytics solutions into production systems.
Provide ad hoc analysis to support business decision-making.
Evaluate business relevance, recommend timing, and support deployment of data-driven solutions.