Work with the team to design and implement features for the data platform.
Automate and optimize data pipelines and set up monitoring and feedback systems
Write and optimize ETL from/to the product and homegrown or 3rd party tools.
Drive vendor POCs for data and analytics solutions.
Translate data consumption requirements into technical specifications, including data streams, integrations, transformations, databases, and data warehouses
Collaborate cross functionally to revamp data models that power business critical internal and customer-visible metrics
Advocate for data driven thinking and help elevate data literacy across the company
Mentor and learn from peers to build a cohesive culture and best practices on data at Eventbrite
THE SKILLSET
6-8 years of experience in Software or Data Engineering, building high quality production software
Experience with modern data ingestion tech stack
Outstanding interpersonal and communication skills
Strong analytical, problem solving and critical thinking skills
Proficiency in Python, SQL Airflow.
Experience running ETL processes on large scale data sets
Experience with modern data warehouses like Snowflake , Amazon Redshift, Google Big-Query. Experience with data governance tools like Collibra, Amundsen or Alation.
Familiarity with Data Engineering tools such as Hadoop, MySQL, Dagster, DynamoDB, EMR, Terraform, Kafka/Kinesis, Presto/Trino, Kubernetes etc.
Data modeling experience and ability to partner with technical and non technical stakeholders
Experience with data lakes based on Amazon S3, HDFS.
Experience translating high level business questions to analytics needs and implementation
Knowledge of any Cloud preferably AWS and Hadoop ecosystem knowledge
Have worked on Agile framework
BONUS POINTS FOR
Experience with AWS products and services
Experience architecting and/or running data infrastructure
Experience with Data Visualization like Tableau, PowerBI.
Active Eventbrite user with a passion for live events
Skilled in various forms of data modeling on structured, semi-structured and unstructured data
Familiarity with server-side frameworks, such as Django, Express, Rails, or .Net.
Familiarity with database optimization and scaling approaches including indexing, partitioning, sharding, clustering, in memory tables, horizontal and vertical scaling.
Familiarity with managing large datasets and understanding the complexities of merging large databases, meeting security audit requirements, and implementing data retention policies.
