Data Engineer-NY, SLC, TX, UT, Dallas, New York City , USA

Position: Senior Data Engineer – Time Series Systems

We are looking for an experienced Senior Data Engineer to lead the design and development of high-performance, scalable data infrastructure for large-scale time series workloads. This role is ideal for someone passionate about building robust systems that power real-time analytics and machine learning.

Key Responsibilities:

• Design, build, and optimize data pipelines to process large volumes of time series data efficiently
• Develop scalable data infrastructure using time series-focused technologies such as KDB+, TimeSet, or Kronos
• Create robust ingestion and transformation workflows to handle both real-time and historical datasets
• Integrate time series systems with Python-based ML pipelines to support training and inference workflows
• Collaborate closely with data scientists and ML engineers to ensure high-quality, accessible data for experimentation and production
• Design data models and schemas tailored for time series use cases, supporting efficient downsampling, indexing, and aggregation
• Monitor and optimize systems for performance, reliability, and scalability
• Establish best practices in data governance, lineage, and observability within large-scale environments
• Mentor junior team members in distributed processing, data architecture, and real-time systems
• Work cross-functionally with product, infrastructure, and engineering teams to align data capabilities with business objectives

Qualifications:

• 5+ years of experience in data engineering, with a strong focus on large-scale and high-throughput systems
• Hands-on experience with time series databases like KDB+, TimeSet, or Kronos
• Proven track record building batch and streaming data pipelines using technologies such as Apache Kafka, Spark, Flink, or AWS Glue
• Proficiency in Python, with experience integrating data pipelines into ML workflows using libraries like pandas, NumPy, scikit-learn, or PyTorch
• Expertise in designing efficient data models and partitioning strategies for time series data
• Solid understanding of distributed systems, columnar databases, and parallel data processing
• Familiarity with cloud-based architectures (AWS, GCP, or Azure) and containerized infrastructure
• Strong skills in data quality, monitoring, lineage, and observability
• Excellent communication and collaboration abilities, particularly in cross-functional or client-facing environments
• Bonus: Experience with multiple time series systems or contributions to open-source data infrastructure projects