Job description

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 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 • 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 #dataengineer • 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

Requirements