Dimitrios M.

Profitero is a global e-commerce analytics platform used by major retail brands to track digital shelf performance, automate insights, and optimize marketplace visibility across Amazon, Walmart, and other major retailers.

• Leads Spark-based ETL optimization initiatives, reducing compute footprint and ensuring SLA-bound ETAs for mission-critical data products.
• Owns the architecture and implementation of high-volume ingestion pipelines running on AWS EMR and Databricks clusters.
• Implements advanced performance tuning, partitioning strategies, caching, and execution-plan refinement across PySpark and Scala Spark workloads.
• Designs Airflow DAGs for fully orchestrated extraction, transformation, model preparation, and delivery processes.
• Collaborates with data scientists and analytics teams to standardize transformation logic into reusable frameworks.
• Ensures production reliability by improving monitoring, logging, and alerting across EMR, Snowflake, and GCP workloads.
• Supports cross-team integration and shapes engineering standards for new data products.

• Built advanced Spark jobs using an internal framework to process multi-source retail datasets at scale.
• Developed complete ETL pipelines—from raw ingestion (S3, APIs, vendor feeds) to cleaning, preparation, and datamart outputs.
• Created recursive custom algorithms to optimize a bottleneck ETL step, improving execution speed and stability.
• Tuned CPU and memory usage, executor configurations, and storage formats to meet strict ETAs.
• Created and maintained Airflow DAGs orchestrating dozens of interdependent tasks.
• Collaborated with product, data, and client teams on integration patterns and requirements alignment.
• Ensured secure distribution of final datasets via SFTP automations.

Mantaro Brands operates and scales multiple D2C consumer brands, relying heavily on data analytics, forecasting, and automated reporting infrastructure.

• Designed and optimized ingestion and transformation pipelines supporting marketplace analytics and brand operations.
• Implemented Spark-based workflows for forecasting, demand analysis, and product performance insights.
• Created Airflow DAGs to orchestrate ETL steps and validation checks.
• Improved Snowflake schema design and warehouse performance for reporting use cases.
• Ensured data readiness and quality for internal analytics and external marketplace integrations.

EPAM is a global engineering and consulting firm delivering cloud, big data, and platform modernization for enterprise clients.

• Built sophisticated Spark jobs on a custom internal framework, executed on AWS EMR clusters.
• Designed ETL flows from heterogeneous data sources, applied cleaning and curation logic, and delivered final datamarts.
• Implemented recursive algorithms for complex transformations, significantly accelerating bottleneck jobs.
• Performed Spark and EMR performance tuning, reducing job runtimes to meet fixed ETAs.
• Developed Airflow DAGs to orchestrate multi-layer pipelines.
• Communicated with customers and cross-functional teams for data integration and requirement analysis.

As Dev Lead:

• Led task allocation, peer reviews, and onboarding for new engineers.
• Provided architecture guidance and ensured alignment with enterprise engineering standards.
• Supported teams with hands-on technical assistance across Spark, Airflow, and AWS EMR.

• Contributed to the implementation of Palantir Foundry for enterprise data governance, modeling, and analytics.
• Built curated datasets, operational workflows, and governed pipelines within Foundry.
• Developed analytics-ready tables and dashboards to support commercial and regulatory operations.
• Collaborated closely with business and data science teams to structure high-value datasets.

Smart Digit is a GE & Rosneft joint venture specializing in industrial data analytics and monitoring platforms.

• Performed exploratory data analysis, hypothesis testing, and business case development.

• Built preprocessing logic, anomaly detection, and outlier identification for high-frequency streaming data.

• Developed dynamic dashboards with Bokeh and custom matplotlib/seaborn visualizations.

• Conducted ML research using shallow models, PCA, Autoencoders, and LSTM networks.

• Provided data insights for operational optimization and predictive maintenance.

Gazpromneft NTC is Gazpromneft's R&D center, responsible for innovation in reservoir simulation and field optimization.

• Analyzed oil field development and reservoir performance data.

• Modeled production scenarios and recommended optimization actions.

• Conducted engineering workflows combining geology, simulation, and field data.

• Collaborated with multidisciplinary teams to improve extraction efficiency.