Back
Remote role, popular Data Engineer title, mid-level experience, and broad tech stack increase candidate competition.
Skills broadly transferable, but lakehouse and streaming experience add moderate industry specificity.
Multiple mandatory technologies and a 5+ years requirement make filters strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Build and maintain high-throughput ETL/ELT data pipelines ingesting data into Lakehouse environments.
Develop modular, reusable code in Python, PySpark, and SQL following CI/CD practices and drive adoption of dbt and PySpark.
Implement monitoring and alerting systems for data SLAs to ensure reliability and perform SQL and Spark query optimization.
Minimum 5+ years of experience in data engineering with on-call system reliability experience.
Expert proficiency in Python, SQL, and PySpark; experience with dbt, Databricks Delta Lake or Snowflake.
Experience with orchestration tools like Apache Airflow or Prefect and infrastructure as code (Terraform or CloudFormation).
Work Mode: Remote.
Experienced in building resilient, self-healing data systems with strong operational ownership including incident monitoring and performance tuning.
Familiar with hybrid compute patterns using both heavyweight (PySpark/Databricks) and lightweight compute (DuckDB/Python) to optimize cost and latency.
Comfortable working with modern Lakehouse architectures and streaming technologies (Kafka, Kinesis, Spark Streaming).