Marina Olina
Verified Expert in Engineering
Data Engineer and Developer
Riga, Latvia
Toptal member since May 30, 2024
Marina is a data engineer with 6+ years of experience and a flair for blending established and emerging technologies to deliver cost-effective and robust solutions. Her innovative approach led to the development of an AWS Glue app, optimizing on-premise migration costs with a Lambda-based alternative. Marina leverages her finance background and expertise in Apache Spark, AWS, Databricks, and Foundry to enhance functionality and efficiency for clients in the fintech and data services sectors.
Portfolio
Experience
- Scala - 6 years
- Terraform - 6 years
- Amazon Web Services (AWS) - 6 years
- Python 3 - 6 years
- Apache Spark - 6 years
- Databricks - 6 years
- Palantir - 3 years
- Azure - 2 years
Preferred Environment
Apache Spark, Amazon Web Services (AWS), Databricks, Foundry, Azure, Snowflake
The most amazing...
...thing I've built is an AWS Glue app for on-premise migration. I used my financial background to propose a cheaper, equally functional alternative using Lambda.
Work Experience
Data Engineer
Self-employed
- Oversaw ETL monitoring, alerting, and post-mortem investigation of issues.
- Tracked and fixed bugs using Jira as a reporting tool.
- Created and fixed BI dashboards, data analysis, and charts.
Data Engineer
C.T.Co
- Conceptualized and architected the pipeline for an Apache Spark application, from initial ideas to MVP, designing scalable and efficient data processing workflows, significantly contributing to the foundational structure and deployment strategy.
- Contributed to enhancing the ETL processes deployed on the Palantir Foundry platform by tuning Apache Spark performance and verifying data integrity.
- Participated in a lift-and-shift project, transitioning from Oracle to Azure Databricks, enhancing scalability and performance.
Data Engineer
Intrum
- Actively contributed to the planning and execution of a comprehensive migration strategy from an on-premises system to a cloud-based infrastructure.
- Served as a monitoring and maintenance team member for a production-grade on-premises Hadoop system.
- Developed proof of concept (PoC) for the lift and shift of an on-premises Hadoop environment to Azure HDInsight.
Data Engineer
Russian Private Gaming Company
- Contributed to the improvement of existing ETL real-time streaming and batch.
- Contributed to DWH concept development/maintenance.
- Created ad hoc data analytics and dashboards to monitor systems and for marketing purposes.
Data Engineer
Accenture
- Collaborated on the creation of new architecture from scratch, using cloud services efficiently.
- Contributed to migration and automation of ETL from on-premises management system to AWS.
- Developed extension of the functionality of separate components.
- Was a member of customer support and maintenance of production solutions teams.
Experience
Clustering for Marketing Purposes
Terraspace Infrastructure
https://github.com/marinaolina/infra/Education
Bachelor of Science Degree in Engineering Economics and Management
Riga Technical University - Riga, Latvia
Certifications
AWS Certified AI Practitioner
Amazon Web Services
AWS Certified Data Engineer Associate
Amazon Web Services
Microsoft Certified: Azure Fundamentals
Microsoft
Chartered Certified Accountant
The Association of Chartered Certified Accountants
Skills
Libraries/APIs
Azure Blob Storage API, PySpark, OpenCV
Tools
Redash, Terraform, Amazon QuickSight, Camunda BPM, GitHub, Azure HDInsight, sparklyr, Dplyr, Git, AWS Glue, Amazon Athena, Kibana, RabbitMQ, Amazon Kinesis Data Firehose, Apache Airflow
Languages
SQL, Python, Snowflake, Pascal, Fortran, BASIC, Scala, Python 3, Bash Script, HTML, R
Frameworks
Apache Spark, Hadoop, Django
Platforms
Databricks, Amazon Web Services (AWS), Azure, AWS Lambda, Apache Kafka, Kubernetes, Jupyter Notebook
Paradigms
Management, Azure DevOps, ETL
Storage
PostgreSQL, Redshift, Amazon DynamoDB, Elasticsearch
Other
Palantir, Data Engineering, Foundry, Economics, Heavy Equipment, Tax Accounting, Reporting, Prometheus, CI/CD Pipelines, Machine Learning, K-means Clustering, Neural Networks, ELT, Tesseract, Amazon Kinesis, Cloud, Artificial Intelligence (AI)
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