Sr. Data Engineer

Build What Matters—Large-Scale Data Engineering Leadership At InRhythm, we don't just build apps—we transform industries. We launch high-impact digital platforms, modernize mission-critical systems, and create human-centered experiences that shape the way people live, work, and thrive. We're seeking a Senior Data Engineer with expertise in large-scale time series data systems to lead the design and implementation of robust, scalable, and efficient data infrastructure. This is a strategic and hands-on role for someone passionate about powering advanced analytics and model-driven decision-making through high-performance data engineering. Who We Are We're InRhythm. We build high-impact digital products that solve real-world problems at scale. Since 2002, we've helped modernize platforms and accelerate innovation for some of the world's most recognized enterprises—including Goldman Sachs, Morgan Stanley, Mastercard, Fidelity, UnitedHealth Group, Amazon, and more. We specialize in web, mobile, cloud-native, and data-intensive systems—but what sets us apart is our culture of craftsmanship, velocity, and outcome-driven delivery. We don't wait to be told what to do. We lead from within. Build the Data-Driven Future Data isn't just infrastructure—it's intelligence in motion. As a Senior Data Engineer, you'll architect and implement large-scale time series data pipelines that support high-throughput ingestion, real-time querying, and seamless integration with Python-based machine learning workflows. You'll work closely with engineering, analytics, and data science teams to ensure data systems are reliable, high-performance, and optimized for large volumes and low-latency workloads. Your work will enable model training, evaluation, and inference on dynamic, continuously evolving datasets that drive real-time insight and innovation. What You'll Do As a Senior Data Engineer at InRhythm, you will: Design, build, and optimize high-performance data pipelines for large-scale time series data Implement scalable data infrastructure using tools such as KDB+, TimeSet (Google's large time series database), or Kronos Develop efficient data ingestion and transformation workflows that handle real-time and historical time series data Connect time series data systems with Python-based model pipelines to support machine learning training and inference Collaborate with data scientists and ML engineers to ensure data availability, quality, and accessibility for experimentation and production Design data models and schemas optimized for time series use cases, including downsampling, aggregation, and indexing strategies Ensure system reliability, scalability, and performance through monitoring, testing, and tuning Establish data governance, lineage, and observability best practices in large-scale environments Mentor junior engineers on large-scale data design, distributed processing, and real-time system architecture Partner with product, engineering, and infrastructure teams to align data systems with business goals Requirements You'll bring: 8+ years of experience in data engineering, with a focus on large-scale and high-throughput systems Deep experience working with time series data and purpose-built storage systems (e.g., KDB+, TimeSet, Kronos) Strong experience building streaming and batch data pipelines using tools like Glue, Kafka, Flink, or Spark Proficiency in Python and integrating data pipelines with machine learning workflows and libraries (e.g., pandas, NumPy, scikit-learn, PyTorch) Experience designing efficient, scalable data models and partitioning strategies for time series data Knowledge of distributed systems, columnar databases, and parallel processing Familiarity with cloud-native data architectures (AWS, GCP, or Azure) and containerized data infrastructure Strong understanding of data quality, lineage, monitoring, and observability tools Excellent communication skills and a proactive, consultative mindset in client-facing environments Bonus: Experience with multiple time series systems (e.g., KDB+ and Kronos) or contributing to open-source data infrastructure projects