Presto

Job Description: Responsibilities ELT Pipeline Engineering: Design, build, and maintain high-throughput batch and streaming ETL/ELT data pipelines using SQL, Python, and PySpark to ingest large datasets into cloud data lakes and Snowflake Lakehouse & Federated Query Operations: Manage and optimize data structures across AWS/Azure storage (S3/ADLS) using open table formats (Apache Iceberg, Delta Lake) and execute cross-platform queries via Presto/Trino Query & Warehouse Optimization: Diagnose performance bottlenecks, optimize SQL queries, and configure Presto/Trino execution parameters alongside Snowflake virtual warehouse sizing/clustering to reduce compute costs and query latency BI & Semantic Layer Development: Build and maintain scalable data models, Looker semantic layers (LookML), and curated Tableau data sources to enable self-service business intelligence and executive reporting Governance & Access Control: Enforce data security and row/column-level access controls using AWS Lake Formation tag-based policies and Snowflake RBAC, while leveraging OpenSearch for real-time log monitoring and search Data Quality & Orchestration: Implement automated pipeline testing, schema validation, and alert monitoring using dbt and Apache Airflow to maintain data freshness and platform integrity Agile Delivery & Collaboration: Active participation in Agile sprint ceremonies, performing code reviews, creating technical architecture documentation, and collaborating with senior engineers on platform features Qualifications Technical Expertise: Core Skills & Languages: Minimum 6 years of hands-on Data Engineering experience with expert-level proficiency in SQL and Python (Pandas, PySpark) Engine & Warehouse Expertise: Strong operational knowledge of Snowflake and PrestoDB for complex, large-scale query processing Cloud & Lakehouse Architecture: Practical experience with AWS or Azure services, cloud storage (S3/ADLS), and columnar/open table formats (Apache Iceberg, Delta Lake, Parquet) BI & Analytics: Hands-on experience developing Looker models (LookML, PDTs) and constructing Tableau dashboards & reporting Transformation & Streaming: Solid background in dbt for transformation, Apache Airflow for workflow DAG management, and familiarity with Apache Kafka for event streaming platforms Applied Data Science Familiarity: Understanding of data preparation requirements for statistical models, machine learning frameworks, and regression/tree-based algorithms Education & Experience: Degree in Data Science, Analytics, Computer Science, or a related quantitative field 6 years of progressive experience building, maintaining, and supporting production data platforms