Job Location: remote
How will you make an impact
- Build and maintain multiple data pipelines to ingest new data sources (API and file-based) and support products used by external users and internal teams.
- Optimize by building tools to evaluate and automatically monitor data quality and develop automated scheduling, testing, and distribution of feeds.
- Work with data engineers, data scientists, and product managers to design, rapid prototype, and productize new data product ideas and capabilities.
- Design and build cloud-based data lakes and data warehouses.
- Conquer complex problems by finding new ways to solve them with simple, efficient approaches focusing on our platforms reliability, scalability, quality, and cost.
- Collaborate with the team to perform root cause analysis and audit internal and external data and processes to help answer specific business questions.
What will you bring to us
- Masters Degree (or a B.S. degree with relevant industry experience) in math, statistics, computer science, or equivalent technical field
- Experience with dimensional data modeling and schema design in a database or data warehouse
- Expertise with scripting languages such as Python and writing efficient and optimized SQL.
- Working experience in building data warehouses and data lakes.
- Experience working directly with data analytics to bridge business requirements with data engineering.
- Experience with AWS infrastructure
- Ability to operate in an agile, entrepreneurial start-up environment and prioritize
- Excellent communication and teamwork, and a passion for learning
- Curiosity and passion for data, visualization, and solving problems
- Willingness to question the validity, accuracy of data, and assumptions
Preferred Qualifications:
- Experience building data warehouse, data lake, and data pipeline using Snowflake/Redshift and other AWS Technologies.
- Experience with large-scale distributed systems with large datasets.
- Experience with event streams and stream processing (e.g., Kafka, Spark, Kinesis)
- Hands-on experience with event streaming with modern event streaming tools like Pulsar, Kafka, and Kinesis. Understanding when streaming vs. batch processing is appropriate, and tradeoffs in a given context
- Knowledge of advertising platforms.
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