Sift

Data Analytics Engineer

United States

Sift Logo
Not SpecifiedCompensation
Senior (5 to 8 years)Experience Level
Full TimeJob Type
UnknownVisa
SaaS, Business IntelligenceIndustries

Requirements

Candidates should possess an advanced SQL skillset with 5+ years of experience, preferably utilizing Snowflake or a similar cloud SQL data warehouse. They should also have 2+ years of experience with a SQL modeling tool such as DBT, SQLDBM, or similar, along with intermediate to advanced Python skills and familiarity with cloud notebooks and Python libraries like pandas, snowflake connector or SQLAlchemy, requests, and json. Furthermore, candidates need 3+ years of experience in developing and maintaining reporting frameworks, with proficiency in BI tools like Looker, Tableau, PowerBI, Sigma, Metabase, or similar, and 2+ years of experience working with CRM systems such as Salesforce or Hubspot. Experience with Airflow or another orchestration tool and Databricks is a plus.

Responsibilities

The Data Analytics Engineer will maintain and expand the Business Operations team’s reporting capabilities through the design of SQL architecture, increase the team’s analytics capacity by introducing or improving automation, and work with team members in Revenue Operations to keep sales reporting up to date. They will also improve and expand monitoring of data quality, exception reporting, and status reporting on all analytics jobs & pipelines, work with Business Operations Analysts to streamline their modeling processes, monitor and troubleshoot data pipeline issues, document data and expand the team’s data dictionary, and support partner teams with reporting frameworks and metrics. Additionally, they will perform ad-hoc analyses or expand existing dashboards as needed.

Skills

SQL
Snowflake
DBT
Python
pandas
Looker
Tableau
PowerBI
Sigma
Metabase
Salesforce
Hubspot
Data Modeling
Airflow
Databricks
B2B SaaS Metrics

Sift

Real-time fraud detection and prevention platform

About Sift

Sift provides a platform focused on detecting and preventing online fraud in real-time, catering to clients in e-commerce, fintech, and digital marketplaces. The platform uses machine learning and artificial intelligence to analyze large datasets, allowing it to identify fraudulent activities effectively. One of its standout features is dynamic friction, which ensures that genuine users have a smooth experience while preventing fraudsters from accessing services. Sift's business model is subscription-based, with fees that depend on transaction volume and service level. Additionally, Sift offers services like chargeback management and dispute resolution, which add further value to its offerings. The company's goal is to enhance digital trust and safety for businesses by providing tools that help them make informed decisions and protect against fraud.

Key Metrics

Bristol, United KingdomHeadquarters
2011Year Founded
$4.4MTotal Funding
SERIES_ACompany Stage
AI & Machine Learning, Financial ServicesIndustries
51-200Employees

Risks

Rise of app-enabled friendly fraud challenges Sift's mobile fraud detection capabilities.
Reliance on third-party delivery apps by QSRs introduces new fraud risks for Sift.
Complex payment processes may complicate Sift's integration and effectiveness in fraud prevention.

Differentiation

Sift offers a comprehensive platform for real-time online fraud detection and prevention.
The company uses machine learning and AI to analyze vast amounts of data effectively.
Sift's dynamic friction feature ensures seamless user experience while blocking fraudsters.

Upsides

Growing demand for AI-driven fraud detection in QSRs presents expansion opportunities for Sift.
Digital-first banks' need for effective authentication aligns with Sift's fraud prevention solutions.
Global trend towards secure payment systems supports Sift's mission for digital trust and safety.

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