Senior Data Engineer
New
Z
Zeely AIAI marketing
Remote-first формат: працюй звідки зручно, але якщо хочеш — будемо раді бачити тебе в наших офісах у Варшаві та КиєвіFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ років досвіду роботи з даними, з яких щонайменше 1,5 року — у ролі Data Engineer
- Required Skills
- PythonSQLAirflowdbt
Requirements
- Have 5+ years of experience working with data in Data Analytics, BI, Data Engineering, or a related area.
- Have at least 1.5 years of experience in a Data Engineer role.
- Have confident SQL skills and use Python to work with large volumes of data.
- Know ETL/ELT processes, API integrations, automation, and data processing.
- Have production experience with dbt and understand how to build analytical models.
- Have experience working with a cloud data warehouse.
- Have experience building, running, and maintaining recurring data processes using Airflow, Prefect, or cloud-native scheduling.
- Understand CDC, replication, backfills, late-arriving data, and common data synchronization issues.
- Apply data quality practices including tests, monitoring, alerts, source reconciliation, and completeness and consistency controls.
- Be ready to take ownership of the stability, transparency, and scalability of analytics infrastructure.
- GCP experience with BigQuery, Cloud Run, Cloud Functions, Pub/Sub, Cloud Scheduler, or GCS is an advantage.
- PostgreSQL DBA experience is an advantage.
Responsibilities
- Develop the company’s analytics infrastructure, including data collection, ETL/ELT processes, orchestration, backfills, and monitoring of critical pipelines.
- Connect and maintain key marketing platform, billing, product event, and internal database sources.
- Build and maintain reliable dbt transformations, foundational models, and prepared datasets for product, marketing, finance, and experimentation analytics.
- Implement data quality tests, validations, reconciliation with platform totals, and controls for freshness, missing data, schema changes, and late-arriving data.
- Document data sources, models, and business logic.
- Help teams use a shared, clear, and reliable data layer.
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