01 / ABOUT ME

Curious by nature. Engineer by practice.

I’m Namazbek Bekzhanov, a data engineer & backend developer based in Almaty, Kazakhstan. I craft high-performance pipelines, optimize SQL engines, and build reliable digital infrastructure.

Almaty, Kazakhstan

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Correctness first.
Fast second.

I treat data systems like banking systems: correctness first, fast second. That mindset turns pipelines into durable products — observable, testable, and trusted.

My work connects data engineering, backend development, and the practical needs of financial infrastructure. Beyond production systems, I share what I learn through teaching and my Telegram channel.

02 / EXPLORE

The journey so far

May 2024 — PresentCurrent

Middle Data Engineer

The National Bank of Kazakhstan (Digital Development Center)

Almaty, Kazakhstan · Full-time

Built a Vertica MPP warehouse for financial reporting and cut analytical query latency.
Built ETL/ELT pipelines in Greenplum from six state systems (ESSP, GKB, ENPF, EIVK, KASE, NPK).
Orchestrate with Airflow and transform with dbt, with data quality checks and monitoring.
Designed a Data Lake on S3/MinIO with Apache Iceberg and Trino for cold data.
Enforce row-level security with Apache Ranger; observability with Zabbix and Grafana.

Sep 2025 — PresentCurrent

Teaching Assistant (Cloud Computing & DevOps)

Kazakh-British Technical University

Bostandıq District · On-site · Part-time

Ran labs + supported lectures on cloud application development.
Guided deployments on GCP (App Engine, Cloud Functions, Cloud SQL).
Reviewed assignments and helped students debug containerized workloads.

Sep 2024 — PresentCurrent

Data Software Engineering Trainee

EPAM Systems

Remote · Internship

Strengthened advanced SQL and data modeling through real-world exercises.
Studied modern DWH/lake patterns and ETL/ELT workflows.
Built analytics/visualization deliverables with cloud-aware practices.

Jun 2024 — Dec 2024

Research Assistant (Computer Vision / ML)

SDU University, Kazakhstan

Almaty, Kazakhstan · Remote · Part-time

Prepared datasets (LabelImg) and trained YOLO detection models.
Improved model quality via structured experiments and metric-driven evaluation.
Optimized training/inference workflows with PyTorch + Scikit-learn.

Oct 2023 — Apr 2024

Core Banking Systems (Colvir CBS) Support Engineer

Bank CenterCredit

Almaty, Kazakhstan · On-site · Full-time

Supported Colvir CBS on Oracle, including EOD batch processing and accounting closings.
Resolved incidents with Oracle SQL and PL/SQL; wrote data correction and validation scripts.
Built automated extraction pipelines and SQL views for live Power BI dashboards.
Worked in Jira, ServiceDesk and Confluence under enterprise SLAs.

Feb 2023 — Sep 2023

Junior Database Engineer

Alma Telecommunications Kazakhstan

Almaty, Kazakhstan · On-site · Full-time

Built C# (.NET) background services for billing files and payment notifications.
Optimized PL/SQL procedures and triggers using execution plan analysis.
Administered Oracle databases: security, integrity checks, index maintenance.

03 / EXPLORE

Always a student

Sep 2025 — Presentcurrent

Master's degree — Computer Science and Data Analytics

Kazakh-British Technical University

Focused on Big Data, Machine Learning, AI, Data Engineering & Analytics, and Software Engineering. Building deeper expertise in Python, ML libraries, and intelligent systems.

Sep 2021 — Jun 2025completed

Bachelor's degree — Information Systems

Suleyman Demirel University

Focused on Software Engineering, Databases, and Computer Networks. Explored Big Data, Blockchain, Web Development, and Linux Administration; built strong foundations in algorithms, probability & statistics, and data engineering concepts.

Off the clock: football fan — teamwork, strategy, and fast decisions under pressure.

Future Goalplanned

Master's Degree — Data Engineering

Technical University of Munich (Germany)

Planning to pursue advanced expertise in Data Engineering, large-scale distributed systems, and modern data architectures at one of Europe's top technical universities.

04 / EXPLORE

Tools of the trade

A toolkit that connects the whole system.

7 toolsets · 35 technologies

Machine Learning & AI

Scikit-learn
TensorFlow
PyTorch
Keras
NumPy
Pandas

Big Data

Spark
Hadoop
Airflow
Kafka

Cold Data

Iceberg
S3
Trino
Debezium

Languages

Python
Go
Java
SQL
Bash

Databases

Postgres
Oracle
MySQL
MSSQL
Vertica
Sybase
Replica

DevOps

Docker
Git
Linux
FastAPI

Environment

PyCharm
DataGrip
DataSpell
DBeaver
Antigravity

05 / EXPLORE

Learning, on record

Google AI Fundamentals

Google · Apr 2026

Credential: ZWLGE1YNC4TM

Certificate of completion: Claude 101

Anthropic · Mar 2026

Credential: stz5kjzvohzj

Google Cloud Big Data and Machine Learning Fundamentals

Google · Sep 2024

Credential: LM5GBC4KKBLT

Software Engineer

HackerRank · Sep 2025

Credential: bd05e4f8a435

Data Engineering

Freedom Holding Corp. · Oct 2024

SQL (Advanced) Certificate

HackerRank

Credential: cba2aa129267

SQL for Data Science

University of California, Davis

Credential: GLYVDBVYEPH7

Python for Data Science

IBM · Feb 2024

Red Hat Certified System Administrator (RHCSA II)

Red Hat · Nov 2023

Red Hat Certified System Administrator (RHCSA)

Red Hat · Oct 2023

Docker Essentials: A Developer Introduction

IBM · Feb 2024

06 / EXPLORE

A clear scope. Reliable data.

Python, SQL, PostgreSQL, dbt and Airflow are at the centre of my data work. Explore the examples below and let’s discuss the problem, the expected outcome and the working schedule.

Explain the slow query.

A scoped review of query plans, expensive operations and candidate changes, with a reproducible measurement plan.

Explore the public Postgres demo

The demo illustrates Postgres query analysis. Oracle, Vertica and Greenplum are separate experience areas.

Make the pipeline easier to trust.

A defined ETL issue, dbt data-quality improvement or Airflow reliability review. Agree on inputs, checks and ownership before implementation.

Explore the public Python ETL demo

A small Python ETL example; the listed tools describe my wider focus, rather than dependencies of this demo.

Measure before changing.

Inspect a pipeline bottleneck, compare approaches and keep a repeatable example of the behaviour.

Explore the public performance demo

Public learning examples. Client results and delivery estimates require an agreed scope.

Discuss a project

THINGS I’M THINKING ABOUT

ETL Apache Spark Apache Kafka Airflow Data Mesh Schema Design T-SQL PostgreSQL Data Warehouse Data Lake Hadoop dbt Data Quality CDC Batch Processing Stream Processing Data Modeling Oracle DB MySQL MSSQL Python FastAPI

Correctness first.Fast second.