Data Analyst Engineer

MAP International•Brunswick, GA

About The Position

We are seeking an experienced Data Analyst/Engineer to lead our data infrastructure modernization and analytics transformation initiatives. This strategic role will architect and implement scalable cloud-based solutions that transform data into actionable insights, driving organizational impact and supporting our growth objectives. As a technical leader, you will design enterprise data systems, champion best practices, and leverage advanced analytics including machine learning and AI to solve complex challenges in our humanitarian mission to deliver medicines globally.

Requirements

AWS Cloud Architecture: Expert-level experience designing and implementing solutions with AWS Athena, S3, Lambda, QuickSight, and additional services (Glue, EMR, SageMaker, Step Functions, CloudWatch)
Programming & Scripting: Advanced proficiency in Python for data engineering, ML/AI development, and automation; expert SQL skills including query optimization, stored procedures, and performance tuning
API Development: Proven experience architecting enterprise API solutions, microservices, and system integrations; familiarity with API gateway patterns and service mesh architecture
Data Modeling: Deep expertise in dimensional modeling, data vault, normalization theory, and database design patterns for both OLTP and OLAP systems
Big Data Technologies: Hands-on experience with distributed computing frameworks (Spark, Hadoop ecosystem) and modern data lake architectures
Machine Learning & AI: Production experience deploying ML models; proficiency with scikit-learn, TensorFlow, PyTorch, or similar; understanding of MLOps principles
DevOps & Infrastructure: Experience with CI/CD pipelines, Infrastructure as Code (Terraform, CloudFormation), Git workflows, and containerization (Docker, Kubernetes)
Data Integration: Expertise with ETL/ELT tools, data orchestration platforms (Airflow, AWS Step Functions), and integration platforms (Zapier, MuleSoft)
7-10+ years of progressive experience in data engineering, analytics, or related technical roles
Demonstrated track record of architecting and delivering enterprise-scale data solutions
Proven experience leading technical initiatives from conception through production deployment
Experience working in cloud-native environments with emphasis on AWS ecosystem
History of delivering measurable business impact through data-driven solutions
Experience partnering with executive leadership and translating business needs into technical strategies
Bachelor’s degree in computer science, Data Science, Statistics, Information Systems, Engineering, or related technical field required
Strategic thinking with ability to align technical solutions to organizational objectives
Exceptional analytical and problem-solving skills with bias toward action
Outstanding communication skills; able to present complex technical concepts to both technical and non-technical audiences including board members and executive leadership
Proven ability to influence and drive change across organizational boundaries
Strong project management skills with ability to manage multiple competing priorities
Leadership and mentoring capabilities; able to guide and develop others
Deep commitment to data quality, security, and ethical use of information
Self-directed with entrepreneurial mindset; thrives in dynamic, mission-driven environments

Nice To Haves

AWS Certifications: Solutions Architect Professional, Data Analytics Specialty, Machine Learning Specialty, or DevOps Engineer Professional
Advanced AWS Experience: Hands-on with Glue, EMR, Redshift, SageMaker, EventBridge, SNS/SQS, CloudFormation/CDK
Enterprise Integration: Experience with Salesforce administration/development, WordPress/WooCommerce ecosystems, Smartsheet automation
Data Visualization: Proficiency with Tableau, Power BI, or similar enterprise BI platforms beyond Quick Sight
Programming Languages: Additional languages such as R, Java, Scala; experience with functional programming concepts
Pharmaceutical/Healthcare: Understanding of pharmaceutical supply chain, regulatory compliance (FDA, DSCSA), or healthcare data standards (HL7, FHIR)
Nonprofit Sector: Experience with nonprofit operations, donor management systems, grant reporting, or impact measurement
Security & Compliance: Knowledge of HIPAA, SOC 2, data privacy regulations (GDPR, CCPA)
Agile Methodologies: Experience with Scrum, Kanban, or other agile frameworks
Thought Leadership: Published articles, conference presentations, or contributions to open-source projects
Master's Degree: In Data Science, Computer Science, Statistics, or related field
Team Leadership: Experience managing or leading technical teams or major initiatives
Master’s degree in data science, Computer Science, Applied Statistics, or MBA with technical focus strongly preferred
Relevant professional certifications (AWS, Google Cloud, Azure, or specialized data/ML certifications) may substitute for advanced degree with equivalent experience
PharmD (Doctor of Pharmacy) combined with technical experience is highly valued - pharmaceutical domain expertise combined with data engineering skills provides exceptional advantage for understanding our medicine distribution operations, regulatory compliance requirements, and supply chain complexities

Responsibilities

Lead the design and implementation of enterprise data architecture supporting organizational strategy
Define and execute data strategy aligned with organizational objectives
Champion data governance, quality standards, and best practices across the organization
Evaluate and recommend emerging technologies to enhance data capabilities
Serve as technical advisor to senior leadership on data-related initiatives
Drive data literacy and analytics adoption across functional teams
Architect and implement enterprise-scale data solutions using AWS services including S3, Lambda, Athena, and related technologies
Design and optimize complex ETL/ELT pipelines handling multi-source data integration at scale
Establish automated data quality frameworks, monitoring systems, and alerting mechanisms
Lead cloud migration initiatives and infrastructure modernization projects
Implement Infrastructure as Code (IaC) using CloudFormation, Terraform, or similar tools
Design cost-optimization strategies for cloud infrastructure and data storage
Ensure data security, compliance, and disaster recovery capabilities
Design and deliver enterprise analytics solutions and executive dashboards using AWS QuickSight
Lead development of organizational KPIs and performance measurement frameworks
Conduct advanced statistical analysis to inform strategic decision-making
Partner with senior leadership to translate business challenges into data-driven solutions
Build self-service analytics capabilities enabling stakeholder independence
Create compelling data narratives and visualizations for board presentations and stakeholder reporting
Architect and implement enterprise API strategy for data access and system integration
Design RESTful APIs following industry best practices and security standards
Lead integration projects connecting internal systems (Salesforce, WordPress, WooCommerce) with external platforms
Establish API governance including versioning, documentation, and lifecycle management
Implement authentication protocols, rate limiting, and comprehensive error handling
Optimize API performance and scalability for high-volume transactional systems
Architect and deploy production-grade machine learning solutions using AWS SageMaker and related services
Lead end-to-end ML lifecycle from problem definition through model deployment and monitoring
Implement MLOps practices including automated retraining, A/B testing, and performance monitoring
Apply AI/ML to optimize operational efficiency, predict demand, and enhance decision-making
Evaluate emerging AI technologies and their applicability to organizational challenges
Establish ethical AI practices and model governance frameworks
Design and implement enterprise data models supporting analytical and operational workloads
Create and maintain dimensional models (star schema, snowflake) optimized for business intelligence
Establish data modeling standards, naming conventions, and documentation practices
Lead data normalization and denormalization strategies based on use case requirements
Design for scalability, handling growth from current volume
Implement advanced optimization techniques including partitioning, indexing, and query tuning
Mentor and guide junior data professionals and cross-functional team members
Lead technical design reviews and code review processes
Collaborate with IT leadership on technology roadmap and digital transformation initiatives
Partner with external vendors and consultants on complex technical implementations
Facilitate knowledge sharing through documentation, training, and best practice dissemination
Architect solutions for processing and analyzing large-scale datasets (multi-terabyte)
Implement distributed computing frameworks and parallel processing strategies
Design data lake architectures balancing performance, cost, and accessibility
Leverage big data technologies (Hadoop, Spark) when appropriate for workload requirements
Optimize storage and compute resources for cost-effective big data processing