Senior Data Engineer - Time Series Systems

ZipRecruiter; New York (NY); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading company is seeking a Senior Data Engineer with expertise in time series systems and large-scale data infrastructure. This role involves designing and optimizing data pipelines to support machine learning and analytics. The ideal candidate will have extensive experience in data engineering and cloud architectures, ensuring high performance and data quality while collaborating closely with data science teams.

Qualifications

• 5+ years of experience in data engineering focused on large-scale systems.
• Deep experience with time series databases like KDB+ and TimeSet.
• Proficient in Python and familiar with ML libraries.

Responsibilities

• Design and build data pipelines for high-performance time series data.
• Integrate data systems with Python-based ML workflows.
• Mentor junior engineers on distributed systems.

Skills

Data Engineering

Time Series Databases

Data Pipelines

Python

Data Governance

Distributed Systems

Tools

KDB+

TimeSet

Kronos

Glue

Kafka

Flink

Spark

Job description

Job DescriptionJob DescriptionSenior Data Engineer – Time Series Systems

Locations: Dallas, TX | New York City, NY | Salt Lake City, UT
Employment Type: Full-Time
Work Authorization: U.S. or Green Card Holders
Work Mode: Onsite (Monday–Friday)
Shift: Standard Business Hours (8 AM – 5 PM)

Position Overview:

We are seeking a skilled Senior Data Engineer with deep expertise in time series data systems and large-scale data infrastructure. This role focuses on designing and building real-time and historical data pipelines that power machine learning and analytics for high-throughput environments. The ideal candidate will bring advanced knowledge of time series storage systems, distributed data processing, and cloud- architectures.

Key Responsibilities:

• Design, build, and optimize high-performance data pipelines for time series data at scale.

• Implement data infrastructure using systems like KDB+, TimeSet, or Kronos.

• Develop real-time and batch ingestion workflows for time series data.

• Integrate time series systems with Python-based ML workflows for training and inference.

• Collaborate with data scientists and ML engineers to ensure data availability and usability.

• Design schemas and data models optimized for downsampling, aggregation, and indexing.

• Ensure high system performance, scalability, and reliability through monitoring and tuning.

• Establish and maintain data governance, lineage, and observability practices.

• Mentor junior engineers on large-scale distributed systems and real-time architecture.

• Align data infrastructure with product and business goals through cross-functional collaboration.

Required Qualifications:

• 5+ years of experience in data engineering with a focus on large-scale and high-throughput systems.

• Deep hands-on experience with time series databases such as KDB+, TimeSet, or Kronos.

• Strong experience building data pipelines using tools like Glue, Kafka, Flink, or Spark.

• Proficiency in Python and familiarity with ML libraries (e.g., pandas, NumPy, scikit-learn, PyTorch).

• Expertise in designing efficient and scalable time series data models and partitioning strategies.

• Strong understanding of distributed systems, columnar storage, and parallel data processing.

• Experience with cloud- architectures (AWS, GCP, or Azure) and containerized environments.

• Strong focus on data quality, monitoring, and operational observability.

• Excellent communication and collaboration skills in technical and consultative environments.

Qualifications:

• Experience working with multiple time series systems or contributing to open-source data infrastructure projects.

• Background in regulated industries (e.g., finance, healthcare) is a plus.

Additional Details:

• Interview Process: 2 Internal Rounds + 2 Client Rounds (All Virtual)

• Background Check: Mandatory post-offer, including criminal history (3-week timeline)

• Important Note: Candidates must not currently or previously have worked with or been placed at Goldman Sachs