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About Our Client
Our client is a leading enterprise data platform company building an open, high-performance data lakehouse for AI and analytical workloads. The platform combines an intelligent SQL query engine, an AI-ready semantic layer, and an open catalog built on Apache Iceberg enabling Fortune 500 companies across finance, energy, manufacturing, and logistics to unify, query, and govern data at massive scale across cloud and on-premise sources.

About the Role
We are looking for a Senior Software Engineer to work on the core query engine of a large-scale distributed data platform. You will own significant components across query planning, optimization, and execution both driving improvements and acting as a trusted owner of production quality: diagnosing complex customer-reported issues and delivering safe, well-tested fixes across supported release lines. Engineers with staff-level scope and ambitions are very welcome.
This is a systems-level, backend engineering role focused on distributed data processing internals not application development or CRUD services.

Responsibilities
Architect, design, and implement query engine components planner, optimizer, execution operators, memory management, and data access layers
Diagnose and resolve complex production and customer-reported issues across the full query path correctness, performance, memory, concurrency, resiliency, and compatibility
Develop, review, and backport fixes across multiple supported release lines; strengthen regression prevention
Write and review high-quality, performance-critical code in Java and/or C++
Analyze query profiles, execution plans, logs, and telemetry to drive performance optimization profiling, benchmarking, and eliminating bottlenecks in latency- and throughput-sensitive paths
Integrate with columnar formats, open table formats, and connectivity drivers
Improve regression tests, diagnostics, and observability; contribute to CI/CD architecture and quality processes in Jenkins
Mentor middle engineers, lead code reviews, and help set technical standards
Debug complex cross-layer issues spanning query planning, distributed execution, memory management, and I/O, partnering with US-based engineering leads

Required Qualifications
B.S. or M.S. in Computer Science, Computer Engineering, or a related field
5+ years in backend / systems software engineering with a track record of owning significant components end-to-end
Strong proficiency in Java or C++ (comfort with both is especially valued), with deep understanding of OOP, systems design, memory management, concurrency, and asynchronous programming
Advanced SQL and solid understanding of query execution internals planning, optimization, operator implementation
Experience building or extending data processing systems: query engines, distributed databases, ETL/ELT, or analytical platforms
Ability to assess change risk in a mature production system; experience supporting software across multiple versions, branches, or customer environments
Hands-on experience with Jenkins and modern CI/CD workflows; Docker and Kubernetes
Hands-on experience with at least one major cloud (GCP, AWS, or Azure)
Confident Git/GitHub workflows and rigorous code-review habits
Comfortable with AI-assisted development workflows using modern AI tools for code comprehension, debugging, and test generation, and critically validating their output
Prior experience mentoring engineers, leading technical designs, or owning components end-to-end
English Upper-Intermediate or higher (B2+) daily written and verbal communication with a US-based engineering team
Availability to work EU business hours shifted 23 hours later for daily overlap with US West Coast mornings

Desired Skills
Apache Arrow (columnar in-memory format) and SQL planner/optimizer frameworks such as Apache Calcite
LLVM-based runtime expression compilation / code generation
Open table formats Apache Iceberg, Delta Lake, or Hudi and columnar file formats such as Parquet, Avro, or ORC
MPP query engines (Presto, Trino, or similar); background with distributed data platforms such as Spark, Snowflake, or Databricks
Deep understanding of query optimization (cost-based, rule-based), vectorized execution, memory management, spilling, and caching
Connectivity drivers: JDBC, ODBC, Arrow Flight
Managed Kubernetes (GKE/EKS/AKS) and multi-cloud exposure; Terraform; cloud object storage (AWS S3, Azure Data Lake Storage, Google Cloud Storage)
SaaS operations experience: incident response, profiling, observability, release and backport processes
Systems-level performance work and open-source contributions to data infrastructure projects

Details
Engagement: Long-term contract
Location: Europe (EU / EEA / UK), remote
Working hours: EU business hours, shifted 23 hours later for daily overlap with the US West Coast team

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