Senior Data Engineer

Fetch•Remote,

6d•$149,523 - $206,578

About The Position

Fetch is looking for a Senior Data Engineer to join a cross-functional product team, working closely with machine learning engineers, backend engineers, and product managers to build robust data infrastructure that powers Fetch’s recommendation and audience targeting systems. In this role, you will take ownership of critical data infrastructure components, including data transformation pipelines, real-time event ingestion systems, and the data contracts that ensure reliability across our platforms. Your work will enable downstream services and applications to access high-quality, low-latency data at scale, processing terabytes of data daily. This position requires deep technical expertise in building scalable, reliable data systems, along with strong collaboration skills to translate cross-functional requirements into durable and well-designed solutions. In this role, you will focus on building data infrastructure that powers Fetch’s recommendation systems. You will partner closely with machine learning engineers to design and implement the platforms and pipelines that enable personalized recommendations at scale. Key areas of focus include: Building and maintaining feature store infrastructure to support efficient feature development, discovery, and reuse across recommendation models. Designing and operating low-latency feature serving systems that power real-time recommendation APIs for both training and inference workloads. Implementing monitoring and quality checks to ensure feature freshness, accuracy, and consistency. Collaborating with ML engineers to understand feature access patterns, model requirements, and latency and throughput needs.

Requirements

6+ years of professional experience in data engineering, building and operating production data systems at scale
Proven experience designing, building, and maintaining scalable batch and real-time data pipelines capable of processing terabytes of data daily
Hands-on experience with modern data processing frameworks such as Apache Spark, Kafka, Flink, Open Table Formats, and modern OLAP databases
Strong foundation in data architecture principles, including data modeling, schema design, and tradeoffs between latency, reliability, and cost
Proficiency in at least one modern programming language such as Go, Python, Java, or Rust, along with strong SQL skills
Experience with Infrastructure as Code tools such as Terraform or CloudFormation in a production environment
Familiarity with CI/CD processes and modern software development lifecycle practices, with an emphasis on shipping incrementally and improving systems over time
Experience implementing data quality controls, including validation, monitoring, and anomaly detection
Ability to take ownership of projects with guidance, driving designs from initial architecture through implementation and adoption
Comfort presenting technical designs, participating in peer reviews, and constructively challenging decisions
Strong collaboration skills with experience working closely with software engineers, machine learning engineers, data analysts, and product partners
Undergraduate or graduate degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience

Responsibilities

Design, build, and operate scalable data pipelines using batch and real-time processing technologies such as Apache Spark, Kafka, Flink, or managed cloud streaming services to process terabytes of data daily
Build data infrastructure that ingests real-time events and stores them efficiently across databases, data warehouses, and data lakes within AWS
Establish and enforce data contracts with backend engineering teams by implementing schema management, data quality checks, and monitoring to ensure pipeline reliability
Make data accessible and consumable for operational services, analytics platforms, and data-intensive product features, balancing latency, freshness, and accuracy requirements
Collaborate closely with backend engineers, machine learning engineers, and product partners to understand data access patterns, system constraints, and quality expectations
Take ownership of significant portions of the data platform architecture, driving design decisions and technical prioritization
Develop tools, frameworks, and recommended patterns that enable rapid development of data products and consistent pipeline deployments
Mentor engineers on data engineering best practices and raise the overall quality bar across the organization
Stay current with emerging technologies in data processing and infrastructure, evaluating their applicability and impact on Fetch systems
Building and maintaining feature store infrastructure to support efficient feature development, discovery, and reuse across recommendation models
Designing and operating low-latency feature serving systems that power real-time recommendation APIs for both training and inference workloads
Implementing monitoring and quality checks to ensure feature freshness, accuracy, and consistency
Collaborating with ML engineers to understand feature access patterns, model requirements, and latency and throughput needs