We are seeking a Senior Software Data Engineer to join our team in a software engineering capacity. This is not a data-science or analytics position centered on ad-hoc data exploration; instead, the role focuses on building software, data processing jobs, and data pipelines consumed by internal and external partners. Data is our main product and first-class citizen, and we value correct, high-quality data as much as clean and maintainable code. Responsibilities Write new data pipelines and jobs to produce new outputs (datasets) in scope of new features development Adopt existing data pipelines to integrate with new org-wide platforms, tools, services, and languages Fix bugs in code and correct data caused by incorrect logic or implementation Perform ad-hoc data exploration, validation, and investigation to help select the right tech design and support Product Management team decisions Monitor and troubleshoot production issues with pipelines owned by the team Develop and adopt data quality checks to monitor data issues in the systems Scope and plan new development, including assessing level of effort and providing timelines Maintain tickets hygiene in Radar (ticketing system) Evolve jobs, apps, and systems to a better state across all aspects: code quality, complexity, maintainability, and documentation Communicate with other data engineers in the team, peer teams (QA, UAT, Platform, etc), project managers, and engineering managers on status, blockers, estimates, and timelines Requirements 3+ years of hands-on experience in the big-data field, including Hadoop (HDFS, YARN or Mesos) and Spark Excellent knowledge and hands-on experience of SQL in context of Big Data: Spark SQL, HiveQL Excellent knowledge of Spark, including ability to understand and optimize Spark execution plans via Spark UI, with upcoming migration to Spark 3 Excellent knowledge of Scala or Java Understanding of batch processing and ETL principles in Data Warehouses Familiarity with data completeness signals and orchestration Knowledge of approaches for historical reprocessing and data correction Skills in handling bad data and late data in inputs and outputs Understanding of schema migrations and datasets evolution Strong speaking English, with ability to rely on information heard verbally in meetings and to explain own ideas clearly to native speakers Capability to learn fast new set of tools and technology used internally at the company: platform services, telemetry providers, Spark-as-a-Service, build system, and more Nice to have Understanding of functional programming ideas and principles Experience in building and using web services Familiarity with any of Teradata, Vertica, Oracle, Tableau Skills in Spark Streaming and Kafka Knowledge of Apache Iceberg, Trino (Presto), Druid, Cassandra, or Blob storage like AWS Experience with Splunk Experience with Snowflake