We are seeking a Senior Data Engineer with a deep understanding of scalable data systems, streaming pipelines, and ML-focused data workflows to help design and implement robust data infrastructure supporting real-time machine learning. In this role, you will work closely with ML engineers, platform engineers, and product teams to build end-to-end data pipelines, feature stores, and monitoring systems that power production-grade recommendation models. Responsibilities and Duties of the Role: -Focus on major areas of work, typically 20% or more of role % of the time. · Design, build, and maintain scalable data pipelines (batch and streaming) for ingesting, transforming, and serving user interaction data, content metadata, and model features. · Develop and operate real-time and offline feature stores to support low-latency ML inference and model training workflows. · Partner with ML engineers to define and implement data schemas, data validation processes, and high-quality datasets for model development. · Implement and maintain data quality monitoring, observability, and alerting to ensure reliable production systems. · Optimize data processing workflows for performance, scalability, and cost efficiency across large-scale datasets. · Collaborate with product managers and ML teams to translate personalization requirements into data infrastructure solutions. · Contribute to the selection and implementation of modern data tooling and cloud-native services. · Participate in code reviews, design discussions, and team knowledge sharing to raise the bar for data engineering excellence. Basic Qualifications: Strong experience with modern data processing frameworks such as Spark, Flink, Beam, Kafka Streams, or equivalent. · Experience designing and implementing real-time streaming data pipelines. · Proficiency with SQL and schema design for large-scale analytical datasets. · Familiarity with cloud data platforms (e.g., AWS) and modern data infrastructure components (e.g., data lakes, data warehouses, feature stores). · Experience supporting ML workflows (model training pipelines, feature engineering, data validation). · Solid software engineering skills with experience in Python, Java, Scala, or similar languages. · Strong problem-solving skills and ability to work independently in a fast-paced environment. Preferred Qualifications: Prior experience building data infrastructure for personalization, recommendation systems, or other ML-powered products. · Familiarity with ML lifecycle tools (MLflow, TFX, Kubeflow) and MLOps best practices. · Experience implementing data validation, monitoring, and lineage tools to ensure high data integrity for ML models. · Knowledge of real-time ML serving architectures and online feature generation. · Experience optimizing large-scale data workflows for latency-sensitive applications. · Prior experience operating in product development or startup environments. · Strong collaboration skills working closely with machine learning engineers and product teams. Experience with: 5+ years of experience building and maintaining production-grade data pipelines and distributed data processing systems. Compensation: The compensation range for this position is $55/hr. - $67/hr. (approx. $110,000 - $135,000 annualized). Final compensation for this role will be determined by various factors such as a candidate’s relevant work experience, skills, certifications, and geographic location. Benefits: Medical, Dental, Vision, Hearing, Employee Assistance Program, Life, AD&D, and Long-term Disability Insurance, Retirement program with a 3% company match (no vesting limitation), interactive employee reward & recognition program, and equipment provision. - EEO Employer - E-Verify Employer