The Data Platform team is responsible for making Siftâs data accessible across a variety of users and use-cases. This team ensures the availability, correctness, and data privacy/ compliance of information critical for Siftâs day-to-day operations. Our customers include not just external clients but also Siftâs data science product teams, our sales, business support services and operations teams. We are super excited about our plans to build our next generation data analytics solution as we approach a phase where we start diving into reporting/visualization and real time accessibility to data across Sift.
What youâll do:
As a Staff engineer on Siftâs Data Platform team, you will build data warehousing and business intelligence systems to empower our customers, engineers, data scientists and analysts to extract insights from our data. You will design and build Petabyte scale systems for high availability, high throughput, data consistency, security, and end user privacy, defining our next generation of data analytics tooling. You will do data modeling and ETL enhancements to improve efficiency and data quality. Youâd enforce best practices on data governance to ensure compliance and data truncation/deletion responsibly. Youâd also have the opportunity to work with console reporting frameworks and build accessible dashboards for both monitoring as well as reporting. A strong staff engineer would also mentor other engineers and promote data engineering best practices across the team and the broader organization.
What would make you a strong fit:
- 2+ years of data modeling experience (Kimball, Imnon or Linstedt)
- Experience writing and optimizing complex ETL pipelines across multiple environments (Dataproc, Notebooks, Snowflake ELT.)
- Experience programming (SQL, Java, Python) and/or utilizing reporting tools (Looker, Tableau, Qlikview, PowerBI)
- Experience designing and building data warehouse, data lake or lake house solutions
- Experience with distributed systems and distributed data storage.
- Experience with large scale data warehousing solutions, like BigQuery, Snowflake, Redshift, Presto, etc.
- Experience working with real time streaming frameworks like Kafka / Kinesis / Spark / Flink
- Experience with data modeling, starting with API design through reporting solutions against it.
- Strong communication and collaboration skills particularly across teams or with functions like data scientists or business analysts.
- Prior experience building and maintaining enterprise analytics environments, including exposure to sales, finance and marketing audiences.
- Experience with Python, Java, or similar OOPS languages
- Experience with cloud infrastructure (e.g. GCP, AWS)
- Experience with workflow orchestrators such as Airflow or Cloud Composer
- Experience with the analytics presentation layer (Dashboards, Reporting, and OLAP)
- Experience with designing for data compliance and privacy
A little about us:
Sift is the leading innovator in Digital Trust & Safety. Hundreds of disruptive, forward-thinking companies like Zillow, and Twitter trust Sift to deliver outstanding customer experience while preventing fraud and abuse.
The Sift engine powers Digital Trust & Safety by helping companies stop fraud before it happens. But itâs not just another anti-fraud platform: Sift enables businesses to tailor experiences to each customer according to the risk they pose. That means fraudsters experience friction, but honest users do not. By drawing on insights from our global network of customers, Sift allows businesses to scale, win, and thrive in the digital era.
Benefits and perks:
- Competitive total compensation package
- 401k plan
- Medical, dental and vision coverage
- Wellness reimbursement
- Education reimbursement
- Flexible time off
Sift is an equal opportunity employer. We make better decisions as a business when we can harness diversity in our experience, data, and background. Sift is working toward building a team that represents the worldwide customers that we serve, inclusive of people from all walks of life who can bring their full selves to work every day.
This document provides transparency around the way in which Sift handles personal data of job applicants: https://sift.com/recruitment-privacy