Description
The company's 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 (> 20TB of data, 220 TB uncompressed).

This data is used to generate reports, update budgets, and drive our optimization engines. We do all this while running against extremely tight SLAs and provide stats and reports as close to Real Time as possible. The most exciting part about working here 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 Streaming 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: Installation, upkeep, maintenance and monitoring of Kafka, Hadoop, Presto, RDBMS Ingest, validate and process internal & third party data Create, maintain and monitor data flows in Hive, SQL and Presto for consistency, accuracy and lag time Maintain and enhance framework for jobs(primarily aggregate jobs in Hive) Create different consumers for data in Kafka using Spark Streaming for near time aggregation Train Developers/Analysts on tools to pull data Tool evaluation/selection/implementation Backups/Retention/High Availability/Capacity Planning Review/Approval - DDL for database, Hive Framework jobs and Spark Streaming to make sure they meet our standards 24*7 On call rotation for Production support Technologies We Use: Airflow - for job scheduling Docker - Packaged container image with all dependencies Graphite/Beacon - for monitoring data flows Hive - SQL data warehouse layer for data in HDFS Impala- faster SQL layer on top of Hive Kafka- distributed commit log storage Kubernetes - Distributed cluster resource manager Presto - fast parallel data warehouse and data federation layer Spark Streaming - Near time aggregation SQL Server - Reliable OLTP RDBMS Sqoop - Import/Export data to RDBMS Requirements BA/BS degree in Computer science or related field 5+ years of software engineering experience Fluency in Python, Experience in Scala/Java is a huge plus (Polyglot programmer preferred!) Proficiency in Linux Strong understanding of RDBMS, SQL; Passion for engineering and computer science around data Knowledge and exposure to distributed production systems ie Hadoop is a huge plus Knowledge and exposure to Cloud migration is a plus WebMD and its affiliates is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law