Data specialist (engineer, scientist, analyst)

Data Engineering & Databases

• Relational: PostgreSQL*, Oracle*, MySQL, MariaDB, TimescaleDB, MS SQL Server
• Analytics: DuckDB, ClickHouse, Redshift
• NoSQL & Specialized: MongoDB, Redis,
  ElasticSearch, GIS, XML (eXist/XQuery)
• Data Warehousing: Large-scale implementations (TB-scale, 3M+ users)
• ETL/ELT: Airflow*, Luigi*, Mara*, Oracle Data Integrator*
• Stream Processing: Apache Kafka, MQTT, RabbitMQ
• Data Quality: Complex GDPR frameworks, healthcare compliance
  Digital Analytics & Marketing

• Google Marketing Platform: GA4*, GTM*, Google Ads integration
• Web Analytics: Custom event tracking, E- commerce tracking
• Alternative Analytics: Matomo*, Custom tracking solutions
• Marketing Data: Multi-channel attribution, Campaign analysis
  Programming & Development

• Python*: Data processing, ML implementation, sensor data
• Java: Spring Framework, Enterprise applications
• Additional: C#, C, Go, R, Haskell
• Web: JavaScript, AngularJS, HTML, CSS
• Build Tools: Maven, Gradle, Gulp
  * Indicates deep expertise/daily usage

Cloud & Infrastructure

• • AWS*: RDS, EC2, S3 (primary platform, daily use)
• • Other Cloud: GCP (Reporting), Azure (Legacy systems)
• • Container/CI/CD: Kubernetes*, Docker*, Jenkins, GitHub Actions, ArgoCD
• • Infrastructure as Code: Ansible, AWS CloudFormation
• • Monitoring: Grafana*, Prometheus*

Machine Learning & Data Science

• • Algorithms: KNN, Random Forests, Auto- encoders, SVM, Regression
• • Deep Learning: PyTorch experience, TensorFlow familiarity
• • Use Cases: Customer behavior prediction, Time series analysis
• • Pattern Recognition: Scientific data, Customer behavior

Business Intelligence

• • BI Tools: Tableau*, Qlikview*, Metabase*, Google Data Studio*
• • Reporting: Custom solutions, KPI implementation
• • Data Dictionary Development
• • Complex Reporting Frameworks

Team Leadership Approach

•  People-First Focus: Individual strength development, team psychology
• • Goal-Oriented Pragmatism: Balancing development with business objectives • Cross-Functional Leadership: Successfully led diverse technical teams

Management Achievements

• • Built and scaled high-performing data teams to 14 members
• • Established comprehensive mentoring programs
• • Created collaborative environments leading to consistent recognition • Implemented effective knowledge sharing practices

Strategic Leadership

• • Budget Management: Infrastructure costs, tool licensing, professional development • Stakeholder Communication: Bridging technical and business perspectives
• • Project Prioritization: Balancing strategic initiatives with operational needs
• • Risk Management: Particularly in sensitive data handling and compliance areas