Junior Data Engineer

Company: CORE TECHNOLOGY Location: Tashkent Salary: from 8,000,000 UZS Experience: From 1 to 3 years

We are looking for a Junior Data Engineer to join our team, which is developing a corporate data warehouse ( DWH) from scratch and is involved in creating regulatory and management data marts.

If you are interested in working with data not just at the level of SQL queries, but also understanding where the data comes from, how it is transformed, validated, and loaded into the DWH, we would be happy to meet you.

📍 Conditions

• Tashkent, office
• Schedule: 5/2, 09:00–18:00
• Full-time employment
• Salary: from 8,000,000 UZS
• Work in the Data / DWH team
• Mentorship and opportunities for professional growth
• Work on a real RegTech project
• Opportunity to develop in the field of Data Engineering / DWH

🎯 Responsibilities

• Develop and maintain the corporate data warehouse ( DWH).
• Create data models and SQL/dbt models based on S2T (Source-to-Target).
• Develop regulatory and management data marts.
• Write and optimize SQL queries, procedures, and functions.
• Configure and support ETL/ELT processes.
• Develop and support tasks in Apache Airflow.
• Conduct data quality checks ( Data Quality).
• Monitor the correctness of data loading and identify the root causes of errors.
• Work with logs and monitor loading processes.
• Participate in working with CDC and data streams.
• Use Git for code management.
• Collaborate with analysts, developers, and other project participants.
• Document developed solutions.

🔴 Required knowledge and skills

Please apply if you have a basic practical foundation in most of these areas.

SQL — mandatory

Must have a solid understanding of:

• JOIN
• GROUP BY
• HAVING
• subqueries
• CTEs
• window functions
• aggregate functions
• filtering and sorting
• understanding NULL
• basics of SQL query optimization
• preferably — simple stored procedures / functions

DWH — mandatory

Candidates should understand:

• what DWH is and why it is used;
• the difference between OLTP and OLAP;
• what fact and dimension are;
• what a data mart is;
• basic data modeling principles;
• how data flows from Source → Staging → DWH → Data Mart.

ETL / ELT — mandatory

Must understand:

• what ETL and ELT are;
• the stages of data extraction, transformation, and loading;
• how the process of loading data from a source to a DWH is built;
• what full and incremental loading are;
• basic principles of data transformation.

Data Quality — mandatory

Understanding of basic checks:

• NULL;
• duplicates;
• record counts;
• data type compliance;
• uniqueness control;
• data reconciliation between source and DWH;
• identification of incorrect or lost data.

Python — mandatory at a basic level

It is not required to be a Python developer, but it is necessary to:

• understand Python syntax;
• be able to work with data;
• understand basic data structures;
• preferably have experience with pandas.

Git — mandatory / or willingness to learn quickly

• understanding of commit;
• branch;
• pull/push;
• basic work with Git/GitHub/GitLab.

Analytical thinking — mandatory

Must be able to:

• understand data structures;
• identify root causes of errors;
• understand business logic of transformations;
• work carefully with requirements;
• independently search for solutions to problems.

🟢 Will be a big plus

Experience or knowledge in:

• dbt
• Apache Airflow
• Oracle
• PL/SQL
• SQL optimization
• Linux
• CDC
• Oracle GoldenGate
• building DWH from scratch
• S2T mapping
• Data Quality frameworks
• experience with Data Marts
• experience with RegTech / banking / financial data

👤 Who we are looking for

We consider:

• Junior Data Engineers;
• graduates of relevant specializations;
• senior year students;
• candidates with internship experience;
• specialists with experience in academic or pet projects in Data Engineering.

Commercial experience is not mandatory if there is a good technical foundation and real projects.

More important for us:

SQL + understanding of DWH + ETL/ELT + analytical thinking + ability to learn quickly.

📈 What will happen after onboarding

In the first few months, you will gradually learn to:

• create dbt models using S2T;
• develop DWH components;
• create and maintain Airflow DAGs;
• perform Data Quality checks;
• work with real data sources;
• find and fix loading errors;
• participate in code reviews;
• independently manage small tasks from initiation to completion.

❗ Important

Before applying, please check yourself:

• I have a solid command of SQL.
• I understand what DWH is and how OLTP differs from OLAP.
• I understand the principles of ETL/ELT.
• I know basic data quality checks.
• I have at least basic Python experience.
• I have worked with Git or am willing to learn it quickly.
• I am ready to work in the office 5/2, 09:00–18:00.
• I am interested in developing specifically in Data Engineering / DWH.