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Remote, mid-level data engineering role with common tech stack and metro hiring attracts many qualified applicants.
Core data engineering skills (PySpark, dbt, Airflow) are highly transferable across industries.
Multiple mandatory technical skills (PySpark, dbt, Databricks, Airflow, K8s) and 5+ years requirements tighten shortlisting.
Build and maintain high-throughput ETL/ELT pipelines using modern lakehouse technologies (Databricks, Snowflake).
Implement monitoring, alerting, and Data SLAs to ensure pipeline reliability and data quality.
Collaborate with BI and Data Science teams to prepare feature-ready datasets and optimize query performance.
5+ years of experience in Data Engineering with hands-on expertise in Python, SQL, and PySpark.
Proficient in modern data stack tools: dbt for transformation, Databricks or Snowflake for lakehouse architecture.
Experience with orchestration tools such as Apache Airflow or Prefect.
Work Experience Required: 5+ years in Data Engineering. Remote work mode.
Strong operational focus on building resilient, self-healing data pipelines with strict CI/CD practices.
Experienced in hybrid compute models balancing heavy jobs (PySpark/Databricks) and lightweight local executions (DuckDB).
Familiar with cloud infrastructure automation (Terraform/CloudFormation) and container orchestration (Kubernetes).