SeniorSoftwareEngineer-DataPlatform

## Accountabilities Design, build, and operate large-scale data ingestion and replication systems from production sources (e.g., relational databases, NoSQL stores, APIs, and event streams) into a modern data lakehouse architecture. Develop and maintain distributed data processing infrastructure using technologies such as Apache Spark and Databricks, ensuring high reliability, scalability, and performance at petabyte scale. Improve platform observability, security, developer experience, and operational excellence across core data infrastructure systems. Build internal tools, libraries, APIs, and frameworks (primarily in Go, Python, or similar languages) to enable efficient and secure data access and processing across engineering teams. Contribute to the design and evolution of lakehouse infrastructure, including data catalogs, metadata systems, orchestration tools, and cloud storage solutions. Collaborate cross-functionally with product engineering, infrastructure, security, data science, and analytics teams to deliver robust, scalable data solutions. Participate in shaping long-term data platform strategy, including support for AI-driven workloads, global scalability, privacy, and emerging data technologies. Requirements: 4+ years of professional software engineering experience in production environments, with a strong focus on distributed systems or data infrastructure. 4+ years of experience building or operating large-scale data platforms, ingestion pipelines, or data lake/lakehouse systems. Strong hands-on experience with Apache Spark or comparable distributed data processing frameworks. Solid experience working within AWS environments, including services such as S3, RDS, DynamoDB, SQS, Kinesis, or Lambda. Proficiency in at least one production-grade programming language such as Go, Python, Scala, or Java. Proven ability to design scalable, reliable systems with strong emphasis on observability, performance, and operational excellence. Experience collaborating