Responsibilities
Contribute to the design and growth of our Data Products and Data Warehouses around Engagement and Retention Analytics and Data Science
Design and develop scalable data warehousing solutions, building ETL pipelines in Big Data environments (cloud, on-prem, hybrid)
Our tech stack includes Hadoop, AWS, Snowflake, Spark and Airflow and languages include Python, Scala
Help architect data solutions/frameworks and define data models for the underlying data warehouse and data marts
Collaborate with Data Product Managers, Data Architects and Data Engineers to design, implement, and deliver successful data solutions
Maintain detailed documentation of your work and changes to support data quality and data governance
Ensure high operational efficiency and quality of your solutions to meet SLAs and support commitment to our customers (Data Science, Data Analytics teams)
Be an active participant and advocate of agile/scrum practice to ensure health and process improvements for your team
Basic Qualifications
6+ years of data engineering experience developing large data pipelines
String Python programming skills
Strong SQL skills and ability to create queries to extract data and build performant datasets
Hands-on experience with distributed systems such as Spark, Hadoop (HDFS, Hive, Presto, PySpark) to query and process data
Preferred Qualifications
Experience with at least one major MPP or cloud database technology (Snowflake, Redshift, Big Query)
Nice to have experience with Cloud technologies like AWS (S3, EMR, EC2)
Solid experience with data integration toolsets (i.e Airflow) and writing and maintaining Data Pipelines
Familiarity with Data Modeling techniques and Data Warehousing standard methodologies and practices
Good Scripting skills, including Bash scripting and Python
Familiar with Scrum and Agile methodologies.