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Remote mid-level Senior Data Engineer with broad modern stack and metro context increases competition.
Core data engineering skills are transferable, but some BPO-specific embedded analytics practices add moderate domain bias.
Explicit 5+ years plus many mandatory technologies (PySpark, dbt, Databricks, Kubernetes) yields strict filters.
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Develop and maintain high-throughput ETL/ELT data pipelines into a Lakehouse environment.
Implement monitoring, alerting systems, and Data SLAs to ensure reliable and high-quality data availability.
Optimize performance of SQL and Spark queries; collaborate with BI and Data Science teams to deliver feature-ready datasets.
5+ years of professional data engineering experience.
Proficiency in Python, SQL, and PySpark.
Hands-on experience with Databricks (Delta Lake) or Snowflake and modern Lakehouse architectures.
Experience with dbt, Apache Airflow or Prefect for orchestration, and familiarity with DuckDB for local development and analytics.
Experienced in building resilient, production-grade data systems with focus on observability and cost-efficient hybrid execution.
Skilled in infrastructure as code (Terraform or CloudFormation) and Kubernetes for deployment and configuration management.
Comfortable balancing heavyweight (PySpark/Databricks) and lightweight (DuckDB/Python) computing to optimize latency and cost.