SeniorSoftwareEngineer-DataPlatform

## Accountabilities Design, build, and operate large-scale data ingestion and replication pipelines from production systems (e.g., databases, APIs, and event-driven services) into a centralized data lakehouse. Develop and maintain distributed data processing infrastructure at petabyte scale using technologies such as Apache Spark, Databricks, and lakehouse frameworks. Improve platform reliability, observability, scalability, security, and developer experience across core data infrastructure systems. Build internal libraries, APIs, and developer tooling (using languages such as Go and Python) to enable efficient, secure, and scalable data access across engineering teams. Contribute to the architecture and evolution of data lake and metadata systems, including catalogs, orchestration frameworks, and storage layers. Collaborate closely with infrastructure, product engineering, data science, security, and analytics teams to deliver robust, production-grade data solutions. Participate in defining long-term data platform strategy, including support for AI workloads, global scale, privacy, and next-generation data products. Requirements: 4+ years of professional software engineering experience in production environments. 4+ years of experience building or operating large-scale data platforms, distributed systems, or data lake/lakehouse infrastructure. Strong hands-on experience with Apache Spark or comparable distributed data processing frameworks. Experience working with AWS cloud infrastructure, including services such as S3, RDS, DynamoDB, SQS, Kinesis, and Lambda. Proficiency in at least one production programming language such as Go, Python, Scala, or Java. Strong systems thinking with the ability to design scalable, reliable, and observable distributed systems. Excellent collaboration skills with cross-functional stakeholders including engineers, data scientists, analysts, and product teams. Nice to have: experience with Databricks, Delta Lake, Iceberg, or Hudi; data