Software Development Engineer II (Data Engineering)

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.

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.

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.

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.

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.