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As an Analytics Engineer within our Customer department, you will lead the way in building and shaping machine learning solutions that delight millions of users worldwide. You will join a collaborative team at the intersection of data engineering and ML, taking ownership of rock-solid end-to-end batch and real-time data pipelines. Working on our in-house data platform, you’ll support ML/AI features that directly improve the experience for our global audience. We care deeply about our customers, and your work ensures our data scientists can rely on high-quality data to meet their needs. While prior AI experience isn't required, we value a determined mindset and an enthusiasm for building rapidly to maximize impact.
Job Responsibility:
Design and maintain scalable end-to-end data pipelines for ML/AI features
Drive excellence by creating robust data models and transformations that handle billions of interactions
Ensure data quality and reliability for ML training and inference pipelines
Keep it simple by implementing best practices for data modeling, testing, and documentation
Move fast by designing agentic workflows that unlock quicker development cycles
Collaborate with Data Scientists and ML Engineers to understand and deliver on data requirements
Partner with Product Managers to translate complex business needs into technical solutions
Contribute to the evolution of our in-house data platform and tooling
Lift each other up by sharing knowledge and standards within our large data community
Communicate technical concepts effectively to both technical and non-technical stakeholders
Requirements:
A strong analytical mindset with the ability to break down complex problems
Experience in building and maintaining data pipelines (ETL/ELT) at scale
Expertise in SQL and proficiency with Python
Experience with modern data stack tools such as BigQuery, dbt, Airflow, or Apache Flink
A deep understanding of data modeling principles and cloud data platforms like AWS or GCP
Experience scaling ML workflows, feature engineering pipelines, and data streaming technologies (e.g., Kafka, Pub/Sub)
Commitment to software engineering best practices including version control, testing, and CI/CD
Act responsibly by ensuring data privacy, security, and ethical standards are met across all data products
Lift each other up through excellent communication, ensuring our cross-functional teams work in sync to surpass the competition
Strong data visualization skills to create clarity for our internal and external partners