Who We Are and What We Build
Flowwow is an international gift marketplace. Underneath it operates the Data Platform, which we built from scratch: not by "setting up managed services", but by designing and writing it ourselves — from the storage layer to AI agents on top of the data.
It's a lakehouse on Apache Iceberg + Trino, with Spark for heavy computations, CDC pipelines on Kafka/Debezium, all in Kubernetes. On top of this is a large layer of proprietary code:
- Our own Data Vault framework — modeling the warehouse as code, not as a set of manual SQL scripts;
- An Airflow wrapper — custom operators, hooks, DAG generation, pipeline testing tools;
- Self-written lineage and data observability tools;
- AI layer: agents on LangGraph + MCP that investigate incidents, analyze data quality, and help the team with routine tasks.
We are a team of engineers, not ETL operators. The majority of our work is Python development: libraries, frameworks, services used by the entire data vertical of the company.
Why This Role Is Interesting
This is a Senior DE position with a strong focus on platform development. Not "writing pipelines based on tickets," but designing tools that make pipelines write themselves orders of magnitude faster — or not at all.
You will work with the lakehouse stack at the internal level: optimizing Trino queries and working with connectors, maintenance of Iceberg tables (compaction, snapshot expiration, position/equality deletes — we know this pain from the inside), schema evolution, REST catalog. This is a depth rarely found in product companies.
And second: we are an AI-native team. Claude Code and similar tools are part of our daily workflow, not a Friday experiment. We are building harness systems around LLMs: agents with tools, code review automation, assistants for writing DAGs. If you are drawn to the state where you design a system, and an agent under your control performs routine tasks — you will like it here.
Responsibilities
- Developing the platform core: Data Vault framework, Airflow wrapper, lineage tools; designing APIs for use by other engineers and responsibility for their development;
- Developing Python libraries and frameworks: data source clients, pipeline testing tools, wrappers for infrastructure components; ensuring code quality through typing, testing, and code review;
- Working with the lakehouse architecture: Iceberg maintenance, table layout optimization, Trino tuning, developing Spark jobs for heavy transformations;
- Building integrations with data sources: OLTP databases, REST/gRPC APIs, Kafka, file storages, SaaS systems; designing unified interfaces for connecting new sources;
- Implementing AI tools in engineering processes: developing agents for incident investigation, data quality analysis, DAG support, and code review;
- Participating in architectural decisions: technology selection, designing interfaces between components, forming and implementing best practices within the team;
- Conducting code reviews and mentoring less experienced colleagues.
Requirements
- 5+ years of industrial experience in data engineering or backend development with a strong data focus: production systems under real load, responsibility for their operation, not just code writing.
- Python is the main requirement. Senior developer level: OOP, library API design, typing, tests, experience writing reusable components used by others.
- Hands-on experience with the lakehouse stack. Apache Iceberg (or Delta/Hudi with readiness to switch quickly): table structure, snapshots, compaction, schema evolution. Understanding what differentiates a lakehouse from a classic DWH beyond a Medium article.
- Trino or another MPP engine: distributed execution architecture, query plan reading, optimization, experience with connectors.
- Deep understanding of Apache Airflow: not just DAG development, but internal workings (scheduler, executors, metadata), experience writing custom operators and hooks.
- Apache Spark: PySpark, Spark SQL, understanding of the execution model.
- Advanced SQL: window functions, CTEs, optimization, execution plan reading, working with large volumes.
- Git, CI/CD — proficiently.
Conditions
- Full-time employment;
- Official employment: labor contract;
- Schedule: 5/2;
- Remote work format;
- Salary level discussed during the hiring stages.
Perks We Offer Our Employees
- Voluntary medical insurance with dental coverage or reimbursement for medical expenses, as well as psychological support services;
- We care about your physical health — we reimburse expenses for sports activities or the purchase of sports equipment;
- We reimburse the cost of equipment you purchase for work;
- We create opportunities for development: we pay for training, conferences, and meetups relevant to your work;
- Access to our corporate loyalty program: personal discounts on Flowwow and offers from partners.
Our Values
Care and Love
Our product is based on people's desire to delight each other and be together. As long as love exists, Flowwow will have users.
Development and Growth
Flowwow is rapidly developing and gladly supports employee initiatives.
Culture of Mutual Help
We employ people who are engaged in the process and care about what they do.
Openness to Change
Everyone can show initiative, offer suggestions, and quickly see their contribution to overall success.
Charity
One of Flowwow's goals is to help people. We are united by social responsibility and a willingness to participate in special charitable events.
