Senior Data Engineer Digital Twin & Operational Modeling

Oteemo, Inc•Falls Church, VA

About The Position

Oteemo is an industry-leading technology consulting firm at the forefront of AI-driven, cloud native, enterprise DevSecOps transformation. We build intelligent, automated, secure systems for organizations tackling their toughest technical and business challenges and we're pushing the boundaries of what AI, generative AI, and agentic systems can do in production, not just in theory. Join us and you'll work alongside recognized experts on cutting-edge projects that blend cloud native architecture, extreme automation, and AI/ML at the core. We foster a dynamic, inclusive, and collaborative culture built on continuous learning, where your ideas shape real outcomes for our clients. If you're passionate about building what's next in AI and cloud technology and want to do it with a team that sets the standard rather than follows it, Oteemo is where you belong.

Requirements

Experience supporting DoD healthcare, or other large healthcare environments.
Experience with digital twins, discrete-event simulation, agent-based modeling, or operations research.
Experience modeling complex, resource-constrained environments such as hospitals, operating rooms, emergency departments, logistics networks, or supply chains.
Familiarity with healthcare operations — OR and surgical case scheduling, patient flow, staffing and resource allocation, room and equipment utilization, supply and logistics management, and capacity planning.
Experience developing optimization, predictive, mathematical, statistical, or machine learning models.
Hands-on experience with simulation and modeling tools such as AnyLogic, SimPy, Arena, MATLAB/Simulink, or comparable Python-based frameworks.
Experience with AWS services including Amazon S3, AWS Glue, AWS Lambda, Amazon Redshift, Amazon RDS, Amazon EMR, Amazon SageMaker, and AWS Lake Formation.
Experience with modern data platforms and tools such as Databricks, Snowflake, Apache Spark, Apache Kafka, or Apache Airflow.
Experience developing APIs and integrating data platforms with enterprise applications.
Ability to obtain and maintain the required DoD security clearance; Public Trust (healthcare) clearance preferred. US Citizen required.
Bachelor's degree in Computer Science, Data Science, Engineering, Operations Research, or a related field, or equivalent practical experience.
7+ years of experience in data engineering, data architecture, analytics engineering, or a related discipline, including hands-on design and implementation of data pipelines and data platforms.
Proven expertise in data modeling, database architecture, ETL/ELT, data integration, and data transformation; experience with relational and/or NoSQL databases; track record designing data solutions that support analytics, modeling, simulation, or operational decision-making.
Experience with cloud-based data platforms, preferably AWS; strong SQL skills plus proficiency in one or more programming languages such as Python, Java, or Scala.
Working knowledge of data quality, governance, metadata management, lineage, and data security practices.
Strong analytical and problem-solving skills; ability to translate complex operational processes into technical, data-driven solutions; comfortable collaborating with data scientists, architects, SMEs, and government stakeholders.

Responsibilities

Design and build scalable data architectures and pipelines that power digital twin, analytics, and modeling initiatives.
Architect ingestion, integration, transformation, storage, and access across structured and unstructured sources.
Integrate operational, clinical, resource, scheduling, and logistics data on cloud-based platforms using modern data engineering practices.
Lead data and model development for a digital twin of the OR environment that represents physical and operational processes through data and computational models.
Partner with DHA and MHS stakeholders to map workflows, resources, constraints, and dependencies.
Translate real-world OR workflows into data models, process models, and simulation models.
Build “what-if” and scenario-analysis capabilities that evaluate changes in staffing, scheduling, room utilization, equipment, patient flow, and capacity.
Apply statistical modeling, optimization, machine learning, or simulation techniques where appropriate.
Analyze large, complex datasets to identify bottlenecks, inefficiencies, and optimization opportunities.
Deliver dashboards, analytical models, and decision-support tools that turn complex data into actionable insights for operational planning.
Design APIs and data services that connect digital twin and analytical capabilities to other enterprise systems.
Establish data quality, governance, lineage, validation, and monitoring processes.
Document architectures, models, pipelines, algorithms, assumptions, and system dependencies.
Communicate complex technical and analytical concepts clearly to technical teams, healthcare stakeholders, and government leadership.