Staff Data Engineer - High-Performance Data Systems
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Next Technology ProfessionalsData engineering
Workplace type: remote; Country code: PLContractStaff
Salary not disclosed
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Job Details
- Required Skills
- PostgreSQLSQLClickhouseRustData modeling
Requirements
- Bring solid experience designing and building data infrastructure, databases, analytical platforms, or high-performance data systems.
- Demonstrate advanced SQL and PostgreSQL knowledge, including query planning, optimization, indexing, and transaction consistency.
- Understand data architecture and modeling, and the differences between transactional and analytical processing.
- Have experience designing systems for large data volumes, complex computations, and low latency.
- Be proficient with AI coding assistants and able to deliver high-quality production code.
- Be ready to make key architectural decisions while actively writing code in a hands-on role.
- Apply rigorous standards to software correctness, reliability, testing, and maintainability.
- Know Rust, particularly concurrency, asynchronous programming, memory management, and performance optimization.
- Apache DataFusion and Apache Arrow knowledge is welcome.
- Experience with ClickHouse or other columnar/OLAP engines, HTAP/Lakehouse architectures, AWS/GCP/Azure, or financial computation is welcome.
Responsibilities
- Design and develop the platform’s data architecture, including data models, schemas, storage strategies, and support for transactional and analytical workloads.
- Define data-processing architecture that balances batch and real-time processing while supporting low latency and horizontal scalability.
- Design and optimize PostgreSQL and analytical data infrastructure, including query performance, indexing, and storage optimization.
- Build and develop a compute engine, including custom query execution and reusable transformations.
- Create semantic views, SQL extensions, and a persistent catalog for data definitions and complex financial calculations.
- Design integrations with external data sources, third-party applications, and AI/ML systems, including CSV and Parquet files.
- Establish standards for numerical correctness, data versioning, schema migrations, data lineage, auditability, observability, and production reliability.
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