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, Big Data, 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 as Data Engineering Manager at Facebook where I build a high-quality DE team supporting FB's Ads & Business Platform.

Career history

Facebook

Facebook

Data Engineering Manager, FEB 2021 - CURRENT

Facebook's mission is to give people the power to build community and bring the world closer together.

I support FB's Ads & Business Platform, more specifically - the backend ads delivery system powering our personalized ads experience. My role responsibilities are:

  • Driving the vision for BI and Data Warehousing across backend ads delivery system
  • Defining and executing a plan to achieve that vision
  • Defining the processes needed to achieve operational excellence in all areas, including project management and system reliability.
  • Building a high-quality BI and Data Warehousing team and designing the team to scale.
  • Building cross-functional relationships with Data Scientists, Product Managers, and Software Engineers to understand data needs and deliver on those needs.
  • Driving the design, building, and launching of new data models and data pipelines in production.
  • Managing the development of data resources and supporting new product launches.
  • Driving data quality across the product vertical and related business areas.
  • Managing the delivery of high-impact dashboards and data visualizations.
  • Define and manage SLA’s for all data sets and processes running in production.

Facebook

Facebook

Data Engineer, JAN 2018 - FEB 2021

I focused on delivering my mission of supporting FB's Ads & Business Platform products by delivering the best data foundation that drives impact through informed decision making. My key responsibilities were:

  • Designing, building, and launching efficient and reliable data pipelines to move data across a number of platforms including Data Warehouse and real-time systems.
  • Communicating, at scale, through multiple mediums: dashboards, reports, presentations, company-wide datasets, bots, and more.
  • Partnering with leadership, engineers, program managers, and data scientists to understand data needs
  • Educating partners by using data and analytics experience
  • Identifying and addressing gaps in existing logging and processes - data road-mapping.

Technologies: Presto, Spark, Hive, Python, internal equivalents of Airflow, Tableau, Jupyter

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

Technologies: Python, Java-Script, PostgreSQL, AWS, Anthena, Redshift, Airflow, Flask, Ansible

ImportIo

Import.IO

Python Data Programmer, SEP 2015 - FEB 2016

Worked with Data Operations Team to create high-quality and structured datasets using web-scraping and data manipulation techniques. Responsibilities:

  • Building python and java-script based web-scrapers to crawl most challenging web services
  • Support and development for internal Data Extraction and Scheduling framework
  • Development of internal productivity and automation tools to scale our operations

Technologies: Python, Java-Script, node-js, Regular Expressions, X-Paths, Jankins

GrangeFencing

Grange Fencing

Data Analyst, JUL 2015 - JUL 2015

Strategical analysis of optimal localization of company’s warehouses in the territory of United Kingdom.

  • Gather and transform business requirements into a computer application
  • Use cluster analysis to improve computational performance
  • Creation of digital maps with the result (proposal of different warehouses locations)
  • Present results to stakeholders

Technologies: Python, Q-GIS

PolitechnikaWroclawska

Wroclaw University of Technology

Python Teacher/Lecturer, MAY 2015 - JUN 2015

  • Running classes and preparing materials for didactic purposes
  • I taught Programming in Python, Spatial analysis with ArcGIS API for Python, SQL

Wizipisi

Wroclaw Institute of Spatial Information and Artificial Intelligence

GIS Programmer, JAN 2015 - JUN 2015

I worked in the Remote Sensing department on a software prototype for automating building detection from high-resolution aerial images. My responsibilities were:

  • Using my Remote Sensing and Image Processing expertise to build an algorithm foundation
  • Evaluating automated detection results. Final tests showed a detection rate in the order of ~98%

Technologies: Python, Q-GIS, SAGA GIS

FGI

Finnish Geodetic Institute

Intern, SEP 2015 - NOV 2015

This internship was the result of the 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

GLL

Tulski S., Bęcek K., Two methods to mitigate InSAR-based DEMs vegetation impenetrability bias, Geomatics, Landmanagement and Landscape No.2 2021, pp. 7–21

Read

Digital elevation models (DEM), including the Shuttle Radar Topography Mission (SRTM), are used in many branches of geoscience as an ultimate dataset representing our planet’s surface, making it possible to investigate processes that are shaping our world. The SRTM model exhibits elevation bias or systematic error over forests and vegetated areas due to the microwaves’ peculiar properties that penetrate the vegetation layer to a certain depth. Numerous investigations identified that the penetration depth depends on the forest density and height. In this contribution, two methods are proposed to remove the impact of the vegetation impenetrability effect.

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

Data Idols

Data Idols Summer School

London, OCT 2021

Summer Bootcamp to learn more about the world of Data Science and transition into the Data world. I run "Advanced SQL" session.

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

Open Data Science Conference (ODSC) is an annual event held internationally. The purpose of ODSC events is to discuss data science and ML topics, as well as provide training sessions. Some of the training sessions covered: 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

harvardX

Harvard University

CS50's Introduction to Artificial Intelligence with Python, NOV 2021

7 weeks course covering baisc of Artificial Intelligence - Search, Knowledge representation, Uncertainty, Optimization, Machine Learning, Neural Networks and NLP.

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 (Airflow, Luigi)

Data Modeling

Data Visualization

Web scraping

Linux, Mac, Windows

Git

Data Manipulation

Big Data (Presto, Spark, Hive)

Machine Learning

JavaScript

Continuous Integration (Jenkins, Ansible)

Data Analysis

Image Processing

AWS

Web development

ArcGIS, QGIS

R