THE ROLE
Software Development Engineer II – Data Engineering
We’re looking for a Software Development Engineer II (Data Engineering) to join our growing Data Engineering team. In this role, you will design, build, and operate scalable data platforms, pipelines, and services that power analytics, engineering, and Data Science across Quince.
You will work closely with engineering and business stakeholders to translate complex data requirements into reliable and scalable technical solutions. You will take ownership of data engineering modules end-to-end, contribute to system design and architecture, and continuously improve the performance, reliability, and scalability of our data infrastructure.
This role is ideal for an engineer who enjoys solving complex data problems, working with large-scale distributed systems, and spending the majority of their time building and improving production systems.
Responsibilities
Data Engineering & Platform Development
- Design, build, and maintain scalable data pipelines, platforms, tools, and services.
- Develop cloud-based data engineering solutions that support analytics, engineering, and Data Science use cases.
- Build reliable data ingestion, transformation, processing, and extraction workflows across multiple data sources.
- Own modules within the Data Engineering platform from design and implementation through production operations.
- Develop clean, maintainable, testable, and well-documented code following engineering best practices.
System Design & Scalability
- Own low-level system design and implementation for data engineering components.
- Design highly available and fault-tolerant services for data ingestion, processing, and extraction.
- Optimize data systems for performance, scalability, reliability, and read/write latency.
- Apply distributed computing principles to build systems that can efficiently process large and complex datasets.
- Identify bottlenecks and continuously improve the efficiency of data pipelines and services.
Big Data & Distributed Systems
- Work with large-scale data processing technologies such as Hadoop, Spark, Hive, and Presto.
- Evaluate, research, and integrate modern data engineering frameworks and technologies as the platform evolves.
- Build and maintain streaming and event-driven data pipelines using technologies such as Kafka, Kinesis, or RabbitMQ.
- Design solutions for processing and integrating data from multiple sources and formats.
Collaboration & Execution
- Partner closely with engineering, analytics, Data Science, and business stakeholders to understand requirements and translate them into scalable technical solutions.
- Work effectively through ambiguity by identifying problems, evaluating trade-offs, and driving solutions to completion.
- Communicate technical decisions and system designs clearly across teams.
- Contribute to code reviews, technical discussions, and engineering standards.
- Spend the majority of your time on hands-on development, technical problem-solving, and research.
Operational Excellence
- Monitor and maintain production data pipelines, services, and workflows.
- Debug and resolve data quality, performance, reliability, and production issues.
- Establish appropriate monitoring and operational practices for critical data systems.
- Continuously improve the reliability, scalability, and operational efficiency of data infrastructure.
Qualifications
Required:
- 4+ years of experience in Data Engineering, Software Engineering, or a related technical field.
- Strong experience with big data technologies such as Hadoop, Spark, Hive, or Presto.
- Experience with streaming and messaging platforms such as Kafka, Kinesis, or RabbitMQ.
- Strong proficiency in at least one programming language such as Python, Java, or Scala.
- Experience building highly available and fault-tolerant services, preferably for data ingestion or data extraction.
- Strong understanding of data warehousing fundamentals and data modelling concepts.
- Strong SQL skills, including experience with technologies such as Spark SQL, HiveQL, T-SQL, or PL/SQL.
- Experience integrating, transforming, and processing data from multiple sources.
- Good understanding of distributed computing and large-scale data processing principles.
- Strong analytical and problem-solving skills with the ability to work with large and complex datasets.
- Experience working with production data systems and troubleshooting performance and reliability issues.
- Ability to work independently while collaborating effectively with cross-functional teams.
Preferred:
- Experience working with cloud-native data services, particularly AWS.
- Experience with MPP data warehouses such as Snowflake or Redshift.
- Strong proficiency with Apache Spark and Apache Kafka.
- Experience with NoSQL databases such as Redis, DynamoDB, or Memcached.
- Experience designing and operating large-scale, highly available data platforms.
- Experience with data quality, monitoring, observability, and production data operations.
- Familiarity with containerization, CI/CD, and modern cloud-native engineering practices.
- Experience working in a high-growth technology, e-commerce, or data-intensive environment.