Marya Golovina, Developer in Yerevan, Armenia
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Marya Golovina

Verified Expert  in Engineering

Data Engineer and Developer

Yerevan, Armenia
Toptal Member Since
November 17, 2022

Marya is an experienced, results-driven data engineer with over eight-years-long IT background across different sectors and architecture domains. With her extensive logistics and warehousing sector knowledge, Marya specializes in solution architecture, including retail distribution, transportation, operational optimization, business-to-business (B2B) software as a service (SaaS), fashion retail, and consumer staples.


Azure, Databricks, Delta Lake, Microservices Architecture, Data Engineering...
Mango Telecom
PostgreSQL, Greenplum, SQL, IT Business Analysis, Data Warehouse Design...
Warden Machinery
Luigi, Flask, Python, SQL, Microservices Architecture, MLflow, DeepStream SDK...




Preferred Environment

Linux, GitLab, Azure, Apache Airflow

The most amazing...

...project I've worked on is related to the cargo delivery process in the Arctic.

Work Experience

Data Engineer

2021 - 2022
  • Maintained corporate data lake, including developing and supporting extract, transform, and load (ETL) pipelines, middleware, and databases.
  • Created a schema describing data flow related to data science projects on all levels: external, conceptual, and physical.
  • Implemented metadata collection to support quality control of Luigi Jobs in a legacy subsystem.
Technologies: Azure, Databricks, Delta Lake, Microservices Architecture, Data Engineering, Linux, Logistics, Luigi, Flask, Docker, Python, FastAPI

DWH Developer

2019 - 2021
Mango Telecom
  • Participated in developing corporate data warehouse (DWH) from user stories to data marts.
  • Maintained ELT scripts with 1,300 tables in my scope of responsibility.
  • Prepared documentation using dynamic Lucidchart diagrams.
Technologies: PostgreSQL, Greenplum, SQL, IT Business Analysis, Data Warehouse Design, Data Engineering, CatBoost, Linux, Luigi, Flask

Data Engineer

2017 - 2019
Warden Machinery
  • Identified client needs and performed market analysis for industrial machine vision applications.
  • Performed system analyses and created solutions design.
  • Developed data pipelines for experiment tracking, microservices, and standalone portals.
Technologies: Luigi, Flask, Python, SQL, Microservices Architecture, MLflow, DeepStream SDK, Docker, Linux, Applied Mathematics, Apache Airflow, Scikit-learn, Machine Learning

Technical Presales Engineer

2017 - 2018
  • Explored client needs and matched technological solutions to those needs as the de facto sales executive assistant.
  • Designed solutions based on business intelligence (BI) and machine learning (ML), analyzed relevant markets, and performed feasibility studies and Python stack data analysis.
  • Wrote functional designs and technical specifications.
Technologies: Systems Analysis, Python 3, Machine Learning, Applied Mathematics, Microservices Architecture, CatBoost, Scikit-learn, PostgreSQL, Logistics


2016 - 2017
  • Gathered functional requirements, conducted task decomposition, and wrote technical specifications for a Microsoft Dynamics customization project.
  • Designed test cases and developed ETL Python and SQL scripts for data consistency validation.
  • Provided third-line support for issues requiring engineering expertise.
Technologies: Python, IT Business Analysis, Systems Analysis, Apache Kafka, SQL, Logistics, Microsoft SQL Server

Support Specialist

2014 - 2016
Luxbase (acquired by CRIF)
  • Provided technical support for both current and past releases of a client-server CRM.
  • Performed troubleshooting, ensured proper documentation of causes, and delivered solutions. Assisted with system design activities and provided support for database administrator (DBA) objects.
  • Wrote SQL queries regarding datasets and reports as required.
Technologies: SQL, Microsoft SQL Server

Cold Chain Monitoring

An expert system that consists of multiple predictive models for solving allocation problems in multi-chiller cooling plants. Components consider weather conditions, probable demand, penalties, and level of control over equipment, with newer units having out-of-the-box Internet of things (IoT) and others having radio-frequency identification (RFID) temperature sensors.

Demand Forecasting Model

Forecasting for DIY and home appliances retail. As a first stage of the project, the current customer's clusterization was validated on stock return data. In the second stage, different approaches to predict the demand were tried, and this survey was crowned with an autoregressive model based on ridge regression.

Our research revealed a significant impact of weather conditions on sales, so this feature was also added to the forecasting model.

Fleet Tracking

An end-to-end data flow realization. This project included deploying API endpoints, Event Hubs, load of raw data, medallion architecture implementation, cleaning data from outliers, normalizing GIS data, and ad hoc analysis of attributes distribution.
2019 - 2022

Bachelor's Degree in Computer Science

Moscow State University - Moscow, Russia

2009 - 2014

Bachelor's Degree in Philosophy

Russian State University for the Humanities - Moscow, Russia


CatBoost, Scikit-learn, Luigi


Apache Airflow


Microservices Architecture




SQL, Python 3, Python


ClickHouse, PostgreSQL, Greenplum, Microsoft SQL Server


Linux, Azure, Apache Kafka, Databricks, Docker


Data Engineering, Data Warehouse Design, IT Business Analysis, Logistics, Applied Mathematics, Delta Lake, MLflow, DeepStream SDK, Systems Analysis, Machine Learning, SQL Server 2015, FastAPI

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