Back
Remote, popular Data Engineer title, and mid-level experience make applicant competition high.
Data engineering skills and tools are broadly transferable across industries, so background sensitivity is low.
Explicit 4+ years requirement plus many mandatory stack components (Spark, Kafka, dbt, BigQuery) increases strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and maintain scalable batch and real-time data pipelines using Kafka, Maxwell, Spark, dbt, and other modern data technologies.
Develop data models and data lake architectures (Medallion Architecture) to create reliable, reusable, high-quality datasets powering analytics, BI, and self-service reporting.
Own end-to-end lifecycle of critical data pipelines ensuring high availability, performance tuning, monitoring, and proactive incident resolution.
4+ years of experience designing and building scalable data platforms, data lakes, and data warehouses.
Proficiency with Apache Spark (Scala or Python), SQL, Kafka, CDC/Maxwell for batch and streaming pipelines.
Experience with cloud-native platforms and technologies such as Amazon S3, BigQuery, Trino, and dbt including development, testing, and documentation.
Work Experience Required: 4+ years
Experienced in building distributed data processing applications with strong focus on performance optimization and cost efficiency.
Skilled at partnering with cross-functional teams (Product, Engineering, Analytics, Business) to translate requirements into scalable data solutions.
Comfortable owning end-to-end data platform improvements including automation, CI/CD workflows, data quality, lineage, and engineering best practices.