Job Description:

Our team is engaged in analytical projects for Ozon's operational business. We work with data from sorting centers and warehouse logistics: we research processes, test hypotheses, create tools for monitoring and decision-making, and automate calculations and reporting.

We don't have a set of monotonous tasks — the team gets involved in new areas, helps to understand complex processes, quickly immerses itself in new subject areas, and brings analytical solutions to a production-ready state. Our clients include both operational teams and warehouse technologists, as well as top managers of the company.

We are looking for a data analyst who will help develop analytical tools for analyzing warehouse and logistics processes: employee productivity, operational quality, compliance with target processes, automation of warehouse sections, deviation control, and calculation of operational metrics affecting the efficiency of the warehouse network.

Description of Projects and Tasks:

1. Data Analysis and Research

You will work with data from various sources: DWH, Kafka topics, APIs, and internal services. You will need to independently understand the data structure, validate the quality of sources, clarify details with developers and business clients, look for patterns, and explain observed deviations in processes and metrics.

Within the tasks, you will need to dive into warehouse and logistics processes: understand how warehouse operations are structured, how goods and containers move between processing stages, which scenarios are target ones, and which are process deviations.

The team is involved in analyzing employee and operational performance, analyzing the speed of various warehouse processing stages, researching the causes of deviations from target processes, monitoring the quality of operations, and analyzing the automation of warehouse sections.

2. Development of Analytical Tools

You will participate in the creation and development of analytical solutions for the operational business: from ad hoc analytics and research tasks to automated pipelines and reporting.

Within the tasks, it is necessary to: • analyze data from different sources and correlate events; • develop methodologies for calculating metrics; • test hypotheses and evaluate the impact of changes on the process; • identify anomalies and deviations; • implement complex business logic in SQL and Python; • automate calculations and regular processes.

Many projects involve analyzing long chains of events and trying to reconstruct the real picture of what is happening in the warehouse based on data from various systems.

3. Automation and Reporting Support

You will automate calculations and regular reporting, develop and maintain data processing pipelines, participate in preparing exports, Excel reports, mailings, and dashboards for various user levels.

The results of the team's work are used for operational decision-making, process control, and evaluating the effectiveness of changes, so it is important to pay close attention to data quality, calculation logic correctness, and automation stability.

4. Working with Clients and Requirements

It is important to be able to understand business processes, clarify requirements, identify potential problems in task definition, and propose correct analytical solutions.

For many tasks, it is not enough to simply build a report — you need to understand the physics of the process, determine how the operation should be performed in reality, and understand which scenarios are permissible and which are deviations or errors in the process.

Many tasks require rapid immersion in new processes and high attention to detail. In your work, it is important not to be afraid to ask questions, verify data correctness, and suggest improvements to existing logic.

Our stack: SQL, Python, AirFlow, ClickHouse, Vertica/Trino, Superset, and Grafana.

We expect you to:

• Have higher education in data analysis, mathematics, economics, or management

• Have worked as a data analyst for at least six months

• Be proficient in SQL and able to write complex queries

• Have used Python for data analysis and processing

• Be able to independently understand code and new Python libraries

• Have worked with BI tools (Superset, Grafana, Tableau, Power BI, or similar)

• Be able to work with large volumes of data and multiple sources simultaneously

• Pay close attention to data quality and be able to perform checks for completeness, consistency, and adequacy

• Not be afraid of uncertainty and be able to ask clarifying questions

• Be ready to quickly learn new processes, sources, and technical limitations

• Be able to structure tasks, meet deadlines, and communicate risks and problems in a timely manner

• Be able to clearly explain conclusions and interact with clients and related teams

It will be a plus:

• Have worked with AirFlow

• Have used ClickHouse, Vertica

• Have worked with gRPC as a data source

• Have worked with Kafka or event-driven data