About Me


If you'd like to know me better...

I am a hardworking, passionate and imaginative data geek. I have worked with various data, such as NASA satellite imagery, transactional databases or unstructured texts to name a few of them. I have specialized knowledge in all stages of data engineering pipelines - integrations with source systems, data modeling, ETL processes and data visualization. As person who comes from scientific background I also have understanding of statistics and machnine learning.

There is never a good time to stop learn, so I spend my free time reading books, following online courses or haking my side-projects. When there is a chance, I like to attend workshops and meetups.

Currently I work at Facebook as Data Engineer where I can use all my strengths to bring the world closer together.

Career history

Facebook

Facebook

Data Engineer, JAN 2018 - CURRENT

Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities — we're just getting started.

WorldRemit

WorldRemit

Big Data Engineer, APR 2016 - DEC 2017

As Data Engineer at WorldRemit, my key responsibilities were:

  • Making information accessible to the business by acquiring and structuring data from external partners
  • Building new data applications to enhance data driven insights
  • Maintaining and replacing legacy ETL and data warehouse code base
  • Building foundational infrastructure for company's Big Data platform
  • Supporting users on-boarding process to data warehouse

ImportIo

Import.IO

Python Data Programmer, SEP 2015 - FEB 2016

At the beginning of my work my main tasks were building python and java-script web-scrapers and screening/formatting gathered data. To accomplish my goals I have used my problem-solving skills and creativity. Often I had to look for different work around to get data I wanted. Along with python and js I used vastly: Regular Expressions and Xpaths.

Soon I have got a new responsibilities, ie. contributing to python data extraction framework. I have written functions (and unit-tests for them) to make data extraction and cleaning simpler and more automated.

During my work I also built 2 UIs. First one was web-application (written in python’s flask) for managing team internal work. The second one was visual tool for creating advanced configuration files for earlier-mentioned extraction framework. For later, I used node-red, which is node-js flow-based programming tool. The idea of the 2nd UI appeared after I won (together with my coworker) one of company’s hackathon. We presented there prototype of tool written in python and it’s tkinter library. After that success, the project landed on the official product road-map.

GrangeFencing

Grange Fencing

Data Analyst, JUL 2015 - JUL 2015

Strategical analysis of optimal localization of company’s warehouses in the territory of United Kingdom. I was responsible for transforming company's abstract calculation algorithm into computer application. I have used Python with pyshp and tkinter libraries. I have also created visual presentation of outcomes on digital maps (for this purpose I used open source GIS software – QGIS).

PolitechnikaWroclawska

Wroclaw University of Technology

Python Teacher/Lecturer, MAY 2015 - JUN 2015

I was running classes and preparing materials for didactic purposes for future GIS specialists. Topics I have covered during my classes included Python language syntax, data types, creating custom functions, conditional statements, loops, writing more complex scripts, basics of OOP and ArcPy library. My goal was to give students a solid foundation for programming in Python and using ArcPy library for spatial analysis.

Wizipisi

Wroclaw Institute of Spatial Information and Artificial Intelligence

GIS Programmer, JAN 2015 - JUN 2015

I was hired to develop algorithm for automate building detection from high resolution aerial images. It was a part of bigger application designed to improve property tax collecting system. Project was innovative because it utilizes the type of images usually not used for this purpose. To create appropriate solution I had to combine knowledge from remote sensing and image processing along with my own ideas. At the end, validation tests showed that my algorithm's detection rate was about 98%.

During my work I also wrote couple of python scripts to automate coworkers task and took part in other projects, eg. updating cities and addresses database or updating National Topographic Database. My role consisted mainly from data entering, but required also writing SQL queries and data munging.

FGI

Finnish Geodetic Institute

Intern, SEP 2015 - NOV 2015

This internship was the result of my scientific research efforts that I made during my master’s studies. I was responsible for terrestrial and mobile laser scanning field measurements and post-processing gathered data in specialized photogrammetric software.

Education

igig_up

Wroclaw University of Environmental and Life Science

Master's degree in the field of Geoinformatics, JUL 2014

Course included modules on rational databases, SQL queries, programming with VBA (digital image processing, eg. transformations, filters and pixel-based calculations) and GIS programming.

I started my scientific projects on the use of ICESat satellite data to measure global tree heights and improving accuracy of SRTM digital elevation model (which is used inter alia by Google Earth)

I have gained the best possible score for my Master's thesis. I investigated there possibility of improving polish spatial database system with ICESat data.

up

Wroclaw University of Environmental and Life Science

Bachelor's degree in the field of Geodesy and Cartography, FEB 2013

Publications

geodeta_okladka

Tulski S., Lidar in space (in Polish), Geodeta 2014, no. 5, pp. 15-17

Preview

ICESat was the satellite mission, whose primary goal was to monitor polar regions. The satellite was also gathering information about height and vertical structure of clouds and land topography. Main objective of this publication was to asses if data acquired by ICESat are useful for polish spatial data system. To accomplish this goal, horizontal and vertical accuracy of ICESat measurements were checked. It was also analyzed if this data can be used for estimating canopy height.

esa_poster

Tulski S., Improvement of Accuracy of the SRTM C-Band Digital Elevation Model Using the ICESat Ground Control Points.

Poster presented at: ESA EO Summer School 2014, 4-14/08, Frascati, Italy

Digital elevation model (DEM), i.e. digital representation of the surface of the Earth, is important data source for most of the Earth sciences. Near-global DEMs like the SRTM C-Band enable to understand the Earth as a complex system. Despite of its numerous applications, the SRTM C-Band tends to overestimate elevations over areas where vegetation is present. A novel approach utilizing ICESat ground control points was developed to remove this positive elevation bias.

Speaker at

pydata_london

38th PyData

London, OCT 2017

Talk about robust extraction of web data with Python. Jupyter notebook from presentation can be found here.

Workshops

odsc

Agile Data Warehouse Design

London, MAR 2017

A 3-day course presented internationally by leading data warehousing experts, covering the latest techniques in data warehousing and BI systems. This course gave me solid background in translating business requirements into efficient and flexible DWH design. Focus was put on planning, designing and developing DWH solution in incremental and agile manner.

odsc

Open Data Science Conference

London, OCT 2016

Some of most interesting lectures and workshops I was able to attend included topis like: reproducing environments with Docker, mathematical fundamentals of neural networks, data science with R, ML with Python for quant trading and telling the story behind your data.

white_october

White October Events

Advanced Javascript Workshops, JAN 2016

The class gave me strong core understanding of the JS language and its execution model. It was driven by exercises and delivered me knowledge required to make effective use of JS on the back- or front-end. Some of inclued topics: scope, closures, functions, data structures, combining OOP and functional programming.

esa

European Space Center

Earth Observing Summer School, Frascati (Rome), Italy, AUG 2014

I was accepted as one of the youngest participants, because of my scientific achievements. Workshops included lectures and practical exercises about vastly understood Earth observing systems and basics of data assimilation and machine learning.

Online courses

mit

Stanford University

Machine Learning, SEP 2018

11 weeks course contains theoretical and practical knowledge about most advanced machine learning algorithms. Some of the covered topics were: supervised/unsupervised ML and building ML systems - debugging, bias/variance, learning curves, error analysis, ceiling analysis and many more.

mit

MIT

Data Analysis for Social Scientists, DEC 2016

12 weeks course provides a chance to learn about methods for using data to answer questions of cultural and economic interest. Course covers basics of statistic: probability, random variables and their distribution, Bayes' theorem and more. Also Machine Learning and Data Visualization topics are covered. During the course R is used.

microsoft

Microsoft

Data Science and Machine Learning Essentials, MAR 2016

In this course, key concepts in data acquisition, preparation, exploration, and visualization where presented. Along with theoretical knowledge, practical examples how to build a cloud data science solution using Azure Machine Learning, R, and Python was introduced.

linux

Linux Foundation

Introduction to Linux, APR 2015

Course contained working knowledge of Linux, navigation through major Linux distributions, system configuration and basic shell scripting.

Skills

Python

SQL

Data Warehousing

ETL

Data Modeling

Data Visualization

Web scraping

Linux, Mac, Windows

XPaths

HTML5

Git

VI

JavaScript

Ansible

Flask, Django

Data Analysis

Regular Expressions

Machine Learning

ArcGIS, QGIS

R