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Remote role, mid-level data engineer title, and broad toolset increase applicant density.
Core data engineering skills are transferable, though BPO-specific streaming and DuckDB usage add moderate domain bias.
Multiple mandatory technologies and a 5+ years requirement make shortlisting highly selective.
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Build and maintain high-throughput ETL/ELT pipelines and ensure clean, reliable data products in a Lakehouse environment.
Implement data observability and monitoring systems for data SLAs to proactively alert on data drift or pipeline failures.
Optimize performance of SQL and Spark jobs, develop local development workflows, and build cost-efficient micro-pipelines using DuckDB and containerized environments.
5+ years of experience in data engineering with on-call system reliability exposure.
Expertise in Python, SQL, PySpark, and advanced experience with dbt for data transformation and modeling.
Hands-on experience with Databricks (Delta Lake) or Snowflake, Apache Airflow or Prefect orchestration, and infrastructure management using Terraform or CloudFormation.
Work mode: Remote; Streaming data experience and Kubernetes orchestration knowledge preferred but not strictly mandatory.
Experienced in building production-grade, modular, reusable data engineering code with CI/CD adherence.
Skilled in hybrid compute strategies balancing heavyweight (PySpark/Databricks) and lightweight (DuckDB/Python) frameworks to reduce costs and latency.
Able to collaborate cross-functionally with BI and Data Science teams to produce feature-ready datasets and embedded analytics for real-time operational environments.