About the role
Staff Software Engineer (Data Platform)
Join our dynamic team at the forefront of cutting-edge technology as we seek a seasoned Staff Data Engineer. Embark on a journey where your deep-rooted expertise in distributed systems, data architectures, and large-scale processing becomes the cornerstone of building high-performance data platforms. This pivotal role demands proficiency in designing and scaling compute and I/O-intensive data systems, ensuring reliability, efficiency, and cost optimization across the data lifecycle.
Responsibilities
- Design and build scalable data platform components for batch and real-time data processing.
- Architect, develop, and operationalize large-scale data systems across ingestion, transformation, and serving layers.
- Build and manage robust data pipelines ensuring high reliability, scalability, and cost efficiency.
- Develop reusable frameworks and tooling to accelerate productivity for data engineers and data scientists.
- Leverage expertise in Python, Airflow, SQL, and cloud platforms to build production-grade data solutions.
- Optimize query performance and data models using strong understanding of columnar OLAP systems such as ClickHouse, Doris, and StarRocks.
- Implement streaming and near real-time data processing systems.
- Translate complex business requirements into scalable and efficient data platform solutions.
- Work collaboratively with cross-functional teams and provide technical leadership and mentorship.
- Drive architectural decisions by evaluating tradeoffs and selecting the right tools for the problem.
Requirements
- Bachelor's Degree in Computer Science, Information Technology, or a similar discipline.
- 8+ years of professional experience in data engineering, backend systems, or distributed systems.
- Proven experience building scalable data platforms and large-scale data systems.
- Strong experience with ETL pipelines, data integration, and workflow orchestration systems such as Airflow or Temporal.
- Hands-on experience in Python and SQL with strong understanding of data warehouse concepts.
- Experience working with distributed OLTP/OLAP databases such as ClickHouse, PostgreSQL, Cassandra, or Elasticsearch.
- Knowledge of messaging and streaming systems such as Kafka.
- Experience with cloud platforms (AWS/GCP) and big data tools such as Spark.
- Strong understanding of columnar storage systems and query optimization techniques.
- Solid understanding of distributed systems fundamentals and associated tradeoffs.
- Experience working with containers and orchestration tools such as Docker and Kubernetes.
- Strong Linux fundamentals and system-level debugging skills.
- Familiarity with modern data architectures such as Lakehouse (Iceberg, Hudi, Delta) is a plus.