Adam is available for hire
Hire AdamAdam Ivansky
Verified Expert in Engineering
Machine Learning Developer
Buffalo, NY, United States
Toptal member since November 6, 2018
Bio
As a highly experienced senior data engineer and tech lead, Adam specializes in designing and building large-scale data pipelines, data warehouses, and robust data infrastructure. With a strong technical foundation in Python, Snowflake, PostgreSQL, and Terraform he has primarily worked with AWS to deliver scalable and efficient solutions. In addition to my data engineering expertise, Adam has developed several web back-end solutions leveraging Django and FastAPI, further expanding his skill set.
Portfolio
Endeavor
Python 3, Snowflake, Amazon Elastic Container Service (ECS), Terraform, Docker...
Apple
Python 3, Python API, Amazon EKS, Docker, Kubernetes, Amazon S3 (AWS S3)...
BJ's Wholesale Club
Jenkins, AWS CLI, Amazon S3 (AWS S3), Redshift, Python 3, Spark...
Experience
- SQL - 9 years
- Python 3 - 6 years
- Spark - 4 years
- Marketing - 4 years
- Machine Learning - 3 years
- Amazon Elastic MapReduce (EMR) - 3 years
- Data Engineering - 3 years
- Recommendation Systems - 2 years
Availability
Part-time
Preferred Environment
Amazon Web Services (AWS), Python, Terraform, Snowflake, PySpark, Amazon Elastic Container Service (ECS), ETL, Django, FastAPI, Streaming Data
The most amazing...
...project I've worked on is developing a Lambda-based ETL pipeline to process 1TB of streamed telemetry data every day.
Work Experience
Senior Data Engineer
2021 - 2025
Endeavor
- Built and designed both batch and streaming ETL pipelines to facilitate the movement of data in and out of the Snowflake data warehouse.
- Made strategic decisions regarding the selection of technology and architecture design.
- Administered the Snowflake account and managed objects using dbt.
- Provisioned resources such as EKS, S3, Lambda, and Transfer Family in AWS accounts using Terraform.
- Designed and built REST APIs and internal websites to expose marketing models across the organization, integrating them with Amazon Cognito and Microsoft Active Directory.
- Integrated Mixtral 8x7b and Snowflake Cortex LLMs into the company data architecture and designed and developed RAG databases for company-specific marketing.
- Developed standards and formalized development processes.
Technologies: Python 3, Snowflake, Amazon Elastic Container Service (ECS), Terraform, Docker, Prefect, Apache Airflow, Data Build Tool (dbt), AWS Cloud Architecture, Database Design, Large Language Models (LLMs)
Data Engineering Tech Lead
2020 - 2021
Apple
- Served as a data engineer in charge of two projects end-to-end. The projects involved collecting data from 3rd-party cloud vendors.
- Developed scheduled ETLs based on Python and Spark that collected data from various APIs and loaded the data to Amazon S3 and PostgreSQL databases. The ETLs were deployed to Airflow and Kubernetes.
- Built a number of APIs that were exposing data from the data warehouse to consumers of the data.
- Created and modified ETLs based on AWS Glue. Created a serverless ETL based on Amazon SQS and AWS Lambda.
Technologies: Python 3, Python API, Amazon EKS, Docker, Kubernetes, Amazon S3 (AWS S3), Amazon Simple Queue Service (SQS), Amazon Elastic MapReduce (EMR), Redshift, PostgreSQL, SQL, Spark, Data Engineering
Data Engineer
2019 - 2020
BJ's Wholesale Club
- Developed an ETL pipeline based on PySpark running on Amazon EMR for the extraction of data from Redshift to S3.
- Contributed to a product recommendation engine based on Spark machine learning.
- Developed a data quality assessment tool in PySpark.
- Owned cloud cost reporting. Managed EMR cluster creation/termination in AWS CLI and AWS console.
- Automated the entire ETL/marketing pipeline using Jenkins.
- Contributed to the algorithm for identifying new prospective members based on 3rd-party data.
Technologies: Jenkins, AWS CLI, Amazon S3 (AWS S3), Redshift, Python 3, Spark, Amazon Elastic MapReduce (EMR), SQL, Data Engineering
Senior Database Marketing Analyst
2017 - 2018
eBay
- Developed targeting scripts for flagship marketing campaigns with an emphasis on email, mobile push notification, social, and on-site channels. The campaigns often targeted over 50 million users and sometimes resulted in over $100,000 in iGMB annually.
- Designed, developed, implemented, and maintained multi-armed bandit algorithms written in Python while adhering to marketing standards and processes within eBay. The algorithm was measured to generate $5 million annually.
- Trained an algorithm for send-time optimization. This has resulted in a 15% increase in click-through-rate in campaigns where it was implemented.
- Assessed existing email, social, and mobile marketing campaigns in terms of KPIs such as iGMB, OR, and CTR.
- Created dashboards in Tableau that reported on the performance of different marketing algorithms I developed.
- Created scripts that moved data between HIVE and Teradata servers.
- Worked with the largest Teradata DWH in the world and often queried tables with 100+ billion rows.
- Communicated with stakeholders across multiple time zones.
Technologies: SQL, TensorFlow, Scikit-learn, Tableau, PySpark, Apache Hive, Python, Teradata, Python 3, Spark, Data Engineering
Machine Learning SW Developer
2016 - 2017
Valeo
- Developed and trained a machine vision algorithm for recognizing pedestrians in front of vehicles, which has been implemented in several vehicle models, including the GM 2019 Chevy.
- Trained an algorithm to detect dirt on camera lenses. This algorithm had a crucial role in supporting other more complex self-driving functionalities.
- Assessed the quality of unstructured annotated video data used for algorithm training.
- Created a script for synchronization of both structured and unstructured data between multiple teams who participated on the project.
- Attended computer science conferences and studied scientific literature to keep up with new machine learning and computer science trends. Engaged in knowledge exchange with other team members.
- Communicated and networked with teammates and stakeholders from France and Ireland.
Technologies: Protocol Buffers, Intel TBB, C++, OpenCV, SQL, MATLAB, Python, Python 3, Data Engineering
Credit Risk Analyst
2014 - 2015
Erste Group
- Calculated risk parameters CCF, LGD, and PD according to BASEL 2.
- Reduced the overall reserve requirements of Erste Bank subsidiaries by over 7% thanks to the improvements in the statistical engine for calculation of risk parameters CCF, LGD, and PD that I have introduced.
- Designed and trained a mathematical model in SAS for prediction of the overall loss in the event of a client default. This helped Erste improve the repossession process and reduce expenses.
- Performed ad-hoc stress tests for Erste subsidiaries. The results were later submitted directly to the European National Bank.
- Assessed risk portfolio stability via bootstrapping and Monte Carlo methods.
- Created interactive dashboards for risk parameter reporting in Microsoft SQL and Excel.
- Developed a data quality testing system in SAS and SQL.
Technologies: Microsoft Excel, MATLAB, Microsoft SQL Server, SAS, SQL
Teaching and Research Assistant
2012 - 2014
University of Rochester
- Conducted teaching and lab lectures for undergraduate students.
- Developed software for the automation of experiments and analyzed data produced by the experiments.
- Authored several scientific papers that are available online.
Technologies: MATLAB, C++, Image Recognition, Pattern Recognition
Experience
Model for Dynamic Content Optimization and Customization
The aim of the project was to increase the click-through rate of eBay coupon campaigns via the use of machine learning. The development of the algorithm was successful, and it was measured to generate a 20% lift in click-through rate and IGMB.
The early version of the algorithm was based on the multi-armed bandit. Later versions made use of contextual NLP-based multi-armed bandit. The algorithm was developed using a combination of Teradata SQL and Python. I also developed an interactive Tableau dashboard in order to monitor the function of the algorithm and to measure the KPI lift that the algorithm was bringing.
The early version of the algorithm was based on the multi-armed bandit. Later versions made use of contextual NLP-based multi-armed bandit. The algorithm was developed using a combination of Teradata SQL and Python. I also developed an interactive Tableau dashboard in order to monitor the function of the algorithm and to measure the KPI lift that the algorithm was bringing.
Model for Pedestrian Detection Intended for Self-driving Vehicles
The project aimed to develop a machine vision algorithm capable of detecting pedestrians in front of a vehicle by analyzing the input from the vehicle camera. The algorithm is now fully functional and embedded into several newer vehicle models, including the GM 2019 Chevy.
The machine learning algorithm we decided to use was the AdaBoost cascade classifier combined with a deep neural network. We wrote the training application from scratch in C++. Training had to be multithreaded in order to be efficient. Testing and validation were done in Python. A large database of annotated video data was used for algorithm training.
The machine learning algorithm we decided to use was the AdaBoost cascade classifier combined with a deep neural network. We wrote the training application from scratch in C++. Training had to be multithreaded in order to be efficient. Testing and validation were done in Python. A large database of annotated video data was used for algorithm training.
Prediction Model
Precise prediction of the total final loss after a client's default is key to reducing the risk associated with different loan products.
I developed a model that relied on the loan-to-value ratio and the value of the collateral. It was done using a combination of SAS and Microsoft SQL Server. The development of the model required extensive data cleaning and data quality testing.
I developed a model that relied on the loan-to-value ratio and the value of the collateral. It was done using a combination of SAS and Microsoft SQL Server. The development of the model required extensive data cleaning and data quality testing.
Product Recommendation Algorithm
Involved in the development of a recommendation engine based on a collaborative filtering model. The engine was capable of recommending even the products that a given customer did not necessarily buy in the past. The solution was implemented in PySpark and was based on MLlib, Spark's machine learning (ML) library.
ETL for Recommendation Algorithm
Developed an ETL in PySpark to transfer data from Amazon Redshift into an Amazon S3 data lake. I also developed code for customer-level data aggregation and historicization. Finally, I assessed data quality and investigated and remediated data quality issues.
Education
2012 - 2014
Master of Science Degree in Physics
University of Rochester - New York, USA
2008 - 2012
Bachelor's Degree in Physics
National University of Ireland, Galway - Galway, Ireland
Certifications
JANUARY 2023 - JANUARY 2026
AWS Certified Developer
AWS
JANUARY 2023 - JANUARY 2026
AWS Certified Cloud Practitioner
AWS
Skills
Libraries/APIs
PySpark, Scikit-learn, TensorFlow, OpenCV, Intel TBB, Amazon EC2 API, Python API, PyTorch
Tools
Amazon Elastic MapReduce (EMR), Apache Airflow, Git, Spark SQL, AWS Glue, Bitbucket, Tableau, MATLAB, Microsoft Excel, Jenkins, AWS CLI, Amazon EKS, Amazon Simple Queue Service (SQS), Terraform, Amazon Elastic Container Service (ECS), GitHub, Prefect
Languages
SQL, Python 3, Python 2, C++14, Python, C++, SAS, Snowflake
Frameworks
Spark, Hadoop, Django
Paradigms
Unit Testing, Agile, Continuous Integration (CI), ETL, Database Design
Storage
Amazon S3 (AWS S3), Teradata, Redshift, Microsoft SQL Server, Apache Hive, PostgreSQL, Data Lakes
Industry Expertise
Marketing
Platforms
iOS, Windows, Linux, Amazon EC2, Spark Core, Docker, Kubernetes, Amazon Web Services (AWS), Visual Studio Code (VS Code)
Other
Data Analytics, Data Engineering, Recommendation Systems, Machine Learning, Data Quality Analysis, Deep Learning, Protocol Buffers, ETL Tools, Physics, FastAPI, Streaming Data, Data Build Tool (dbt), AWS Cloud Architecture, Large Language Models (LLMs), Image Recognition, Pattern Recognition, Object Detection, Neural Networks
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