Senior Data Engineer, DPD Team

Sr. Data Engineer Our Data Engineering team plays a key role in our technology company that’s experiencing exponential growth. Our data pipeline processes over 80 billion impressions a day (> 20 TB of data, 200 TB uncompressed). This data is used to generate reports, update budgets, and drive our optimization engines. We do all this while running against tight SLAs and provide stats and reports as close to real-time as possible. The most exciting part about working at in our company is the enormous potential for personal and professional growth. We are always seeking new and better tools to help us meet challenges such as adopting proven open-source technologies to make our data infrastructure more nimble, scalable and robust. Some of the cutting-edge technologies we have recently implemented are Kafka, Spark Streaming, Presto, Airflow, and Kubernetes. What you'll be doing: - Design, build, and maintain reliable and scalable enterprise-level distributed transactional data processing systems for scaling the existing business and supporting new business initiatives - Optimize jobs to utilize Kafka, Hadoop, Presto, Spark, and Kubernetes resources in the most efficient way - Monitor and provide transparency into data quality across systems (accuracy, consistency, completeness, etc) - Increase accessibility and effectiveness of data (work with analysts, data scientists, and developers to build/deploy tools and datasets that fit their use cases) - Collaborate within a small team with diverse technology backgrounds - Provide mentorship and guidance to junior team members Team Responsibilities: - Ingest, validate and process internal & third party data - Create, maintain and monitor data flows in Python, Spark, Hive, SQL and Presto for consistency, accuracy and lag time - Maintain and enhance framework for jobs(primarily aggregate jobs in Spark and Hive) - Create different consumers for data in Kafka using Spark Streaming for near time aggregation - Tools evaluation - Backups/Retention/High Availability/Capacity Planning - Review/Approval - DDL for database, Hive Framework jobs and Spark Streaming to make sure they meet our standards Technologies We Use: Python - primary repo language Airflow/Luigi - for job scheduling Docker - Packaged container image with all dependencies Graphite - for monitoring data flows Hive - SQL data warehouse layer for data in HDFS Kafka - distributed commit log storage Kubernetes - Distributed cluster resource manager Presto/Trino - fast parallel data warehouse and data federation layer Spark Streaming - Near time aggregation SQL Server - Reliable OLTP RDBMS Apache Iceberg GCP - BigQuery for performance, Looker for dashboards Requirements 8+ years of data engineering experience Strong skills in and current experience with SQL and Python Strong recent Spark experience (3+ years) Experience working in on-prem environments Hadoop and Hive experience Experience in Scala/Java is a plus (Polyglot programmer preferred!) Proficiency in Linux Strong understanding of RDBMS and query optimization Passion for engineering and computer science around data East Coast U.S. hours 9am-6pm EST; you can work fully remotely Notice period needs to be less than 2 months (or 2 months max) Knowledge and exposure to distributed production systems i.e Hadoop Knowledge and exposure to Cloud migration (AWS/GCP/Azure) is a plus