Senior Backend Engineer (Data Platform)
Posted Jun 5 · 0 applicants
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The role in plain words
This role involves building backend systems and data infrastructure that handle how data is collected, processed, and served across the product. Day-to-day responsibilities include designing end-to-end data pipelines, working with lakehouse technologies, and managing cloud-based production databases. You will also focus on architecture, system reliability, and long-term scalability.
- 6+ years of experience building backend systems in production
- Strong hands-on experience with Python or a similar language
- Experience designing and building data pipelines end to end, including ingestion, processing, and serving
- Solid understanding of data modeling and the differences between transactional and analytical systems
- Experience working with lakehouse technologies such as Iceberg, Athena, Delta Lake, and data warehouse technologies such as Snowflake, BigQuery, Redshift
- Experience working with multi-cloud or cross-environment data systems
- Familiarity with data formats like Parquet and concepts such as partitioning and schema evolution
- Experience building internal data platforms or developer-facing infrastructure
- Hands-on experience with AI tools such as Copilot or Cursor
Extracted from the job description · kept up to date automatically
Who this suits
This role suits experienced backend engineers with at least 6 years of production experience who excel at data pipeline design, data modeling, and working with complex distributed data systems. It is less ideal for junior developers or those looking for strictly defined tasks without system ownership.
Full job description
Original listing · kept for referenceOur team is looking for a Senior Backend Engineer who enjoys working on complex, data-heavy systems and thinking beyond the immediate task. This role sits between backend engineering and data infrastructure, and focuses on building the systems that power how data is collected, processed, and used across the product. How we work We value engineers who take ownership, ask good questions, and are comfortable working in areas that are not fully defined. People who succeed here are those who understand how systems behave in production and think about the long-term impact of their decisions. If you enjoy building systems that deal with real data complexity and want to influence both architecture and product, this role is a good fit.
Requirements: • 6+ years of experience building backend systems in production • Strong hands-on experience with Python or a similar language • Experience designing and building data pipelines end to end, including ingestion, processing, and serving • Solid understanding of data modeling and the differences between transactional and analytical systems • Experience working with lakehouse technologies such as Iceberg, Athena, Delta Lake, and data warehouse technologies such as Snowflake, BigQuery, Redshift • Experience with production databases such as Postgres and at least one NoSQL system • Familiarity with distributed data processing tools such as Spark or Dataflow • Experience with workflow orchestration tools such as Airflow or Temporal • Experience working in cloud environments such as AWS, GCP, or Azure • Strong system design skills and the ability to think through scale, reliability, and data correctness Nice to have • Experience working with multi-cloud or cross-environment data systems • Familiarity with data formats like Parquet and concepts such as partitioning and schema evolution • Experience building internal data platforms or developer-facing infrastructure • Hands-on experience with AI tools such as Copilot or Cursor
Questions about this role
- This listing did not state a salary. We only show pay when the employer publishes it.
- Hybrid
- 6+ years of experience building backend systems in production, Strong hands-on experience with Python or a similar language, Experience designing and building data pipelines end to end, including ingestion, processing, and serving, Solid understanding of data modeling and the differences between transactional and analytical systems, Experience working with lakehouse technologies such as Iceberg, Athena, Delta Lake, and data warehouse technologies such as Snowflake, BigQuery, Redshift