Kemal Güçhan Kar, Developer in Brussels, Belgium
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Kemal Güçhan Kar

Verified Expert  in Engineering

Bio

Kemal is a data analyst and BI developer with close to five years of experience. His SQL skills and experience with Looker are exceptional, and he has done end-to-end data analysis, including production and storage of raw data, ETL processes, storage of analytics, and data-based storytelling using several BI tools. He has also worked on data engineering within AWS. Kemal's database development experience is backed by a bachelor's degree in industrial engineering.

Portfolio

Self-employed
SQL, Python, Meltano, ETL, PL/SQL, Business Intelligence (BI), Airbyte...
Zoetis - Main
SQL, Tableau, Data Analysis, Data Analytics, Financial Modeling, Data Modeling...
The Estée Lauder Companies
Looker, Business Intelligence (BI), SQL, ETL, Data Pipelines, Google BigQuery...

Experience

  • Tableau - 4 years
  • Data Engineering - 4 years
  • Data Analysis - 4 years
  • SQL - 4 years
  • Data Build Tool (dbt) - 3 years
  • Looker - 3 years
  • ETL Tools - 2 years
  • Segment.io - 2 years

Availability

Part-time

Preferred Environment

Looker, Tableau, SQL, PL/SQL, Redshift, Amazon Web Services (AWS), Product Analytics, Amplitude, ETL Tools, Fivetran

The most amazing...

...thing I developed was a data model attributing upsells (of a CRM tool) to product features based on usage, giving valuable insights on the role of features.

Work Experience

Data Engineer and Analyst

2024 - PRESENT
Self-employed
  • Performed ETL using Airbyte and Meltano and maintained pipelines.
  • Built and maintained data models using DBT and Hydra (PostgreSQL-based DB).
  • Designed and developed dashboards using both Superset and Tableau.
Technologies: SQL, Python, Meltano, ETL, PL/SQL, Business Intelligence (BI), Airbyte, Data Build Tool (dbt), YAML, YAML Pipelines, PostgreSQL, Open Source, Superset

Data Analyst and Engineer

2022 - 2023
Zoetis - Main
  • Developed several Tableau dashboards to monitor a targeted segment of customers' sales, purchase behavior, how they compare to other segments, etc.
  • Created data models to support and enhance the functionality of the aforementioned dashboards.
  • Built ETL pipelines in Azure Databricks to keep data up to date for aforementioned dashboards.
Technologies: SQL, Tableau, Data Analysis, Data Analytics, Financial Modeling, Data Modeling, Databricks, Azure, Python

BI Data Developer

2022 - 2022
The Estée Lauder Companies
  • Developed a data model to attribute revenue to virtual selling areas and created a dashboard that enables business users to make data-driven decisions.
  • Recreated old custom dashboards for eCommerce sales in Looker.
  • Created a flexible data model for the calendar in LookML, which the company plans to use in its other projects.
Technologies: Looker, Business Intelligence (BI), SQL, ETL, Data Pipelines, Google BigQuery, Google Analytics, LivePerson, Appointment Booking, eCommerce, BigQuery

Product Data Analyst

2020 - 2022
Teamleader
  • Developed a complex data model in Looker to understand the ROI for product features and created dashboards based on the model. The product had 11,000+ customers and 50,000+ users.
  • Administrated a data analytics cluster, ETL tools (Fivetran and Stitch), Looker, Segment, and Amplitude.
  • Built custom ETLs between Amazon S3 and Redshift to copy and load data and developed scheduled procedures with PL/SQL.
  • Ran Python scripts in Redshift to transform the data for use in Looker.
  • Tracked 10+ new product releases, how users adopted the products, and their ROIs via Looker and Amplitude.
  • Created 5+ A/B tests to help product managers and teams make decisions on new releases via Looker and Amplitude.
  • Migrated from Amazon Redshift to Snowflake after comparing two warehouses in terms of cost and performance.
Technologies: Looker, SQL, PL/SQL, Redshift, Fivetran, Stitch Data, Jira, Agile, Amazon S3 (AWS S3), Amazon RDS, Data Modeling, Dashboards, ETL Tools, Segment, Amplitude, Amazon Web Services (AWS), Python, A/B Testing, Data Analytics, Business Intelligence (BI), Data Analysis, Attribution Modeling, Data Visualization, Relational Databases, APIs, ETL, Microsoft Excel, Data Engineering, Snowflake, Data Warehousing, Data Warehouse Design, Query Optimization

SQL Developer | Data Analyst

2017 - 2020
Roketsan
  • Developed data models for several custom Oracle EBS applications.
  • Built custom Oracle EBS applications with PL/SQL and Oracle Forms; for example, an application for tracking order shipments.
  • Developed a generic workflow with Oracle Workflow, which helped people build custom workflows in one-tenth of the time they were spending.
  • Wrote web services and PL/SQL to assist in integrating Primavera projects with Oracle EBS.
  • Developed several dashboards with Tableau based on PO and PA module data in Oracle.
Technologies: Oracle EBS, SQL, Oracle PL/SQL, Tableau, Toad, Data Analysis, Oracle Forms, Oracle Workflow, Data Analytics, Data Modeling, Business Intelligence (BI), Oracle Primavera, Dashboards, Oracle, Data Visualization, Relational Databases, ETL, User Experience (UX), Microsoft Excel, Data Engineering, Data Warehousing, Data Warehouse Design, Query Optimization

Experience

Upsell Attribution to Product Features

Customers of a CRM company with more than 11,000 customers and 50,000 users upgrade to higher packages around 100 to 150 times per month. I developed a data model and dashboards that assist the company in attributing these upgrades and the gained revenue from these upgrades to product features based on usage after the upgrades. This provided valuable insights on whether the investments in the features were worth it. The client has highly appreciated this project and commented that the CRM company was among the first to provide this kind of analysis.

User Sessions in a Web Application

A data model I created to help the CRM company understand what users' sessions look like based on in-app events that are triggered when a user takes an action. Grouping these events based on their time helped us understand the first and last things users are doing in their sessions, how long sessions last, and a lot of other metrics that provide insights on the user experience.

Virtual Selling Areas' Performance

A data model that helps compare different virtual selling areas such as online appointments, live videos, chat agents, and much more. This data model helps business users calculate return on investment (ROI) by seeing how these virtual selling areas affect average order value (AOV), revenue, new users, retention, and a couple more business metrics.

Education

2012 - 2017

Bachelor's Degree in Industrial Engineering

Middle East Technical University - Ankara, Turkey

Skills

Libraries/APIs

Segment.io

Tools

Tableau, Looker, Toad, Oracle Workflow, Microsoft Excel, Oracle Forms, Stitch Data, Jira, Google Analytics, BigQuery, Superset

Languages

SQL, Snowflake, Python, YAML

Paradigms

Business Intelligence (BI), Agile, ETL

Storage

Oracle PL/SQL, PL/SQL, Relational Databases, Amazon S3 (AWS S3), Redshift, Data Pipelines, PostgreSQL

Platforms

Oracle, Amazon Web Services (AWS), Databricks, Azure, Meltano, Airbyte

Other

Fivetran, Product Analytics, Data Analysis, Amplitude, Data Modeling, Dashboards, Segment, Data Analytics, Data Visualization, Dashboard Development, Amazon RDS, ETL Tools, Statistics, Oracle EBS, A/B Testing, Attribution Modeling, APIs, Data Engineering, Data Warehousing, Data Warehouse Design, Query Optimization, Data Build Tool (dbt), Oracle Primavera, User Experience (UX), Google BigQuery, LivePerson, Appointment Booking, eCommerce, Financial Modeling, YAML Pipelines, Open Source

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