Data Engineer - Biosensors

SGS Consulting; New York (NY); On-site; USD 85,000 - 125,000; Full time

Job summary

SGS Consulting is seeking a data collection specialist to lead sensor-based studies in New York. You will work with research and product teams to gather high-quality biosensor data, ensure data quality across IMU/EMG/optical streams, and troubleshoot data acquisition hardware and software.

You will design distributed compute workflows for analysis, build benchmarking tools, and generate visual summaries to guide decisions.

Qualifications

• Experience with hardware sensors and data collection infrastructure.
• Time-domain signal processing and/or medical imaging experience.
• Proficient Python and SQL with data extraction and visualization.
• Experience with NumPy, SciPy, Pandas and visualization libraries.

Responsibilities

• Design, plan, and execute data collection studies with research/product teams.
• Monitor data quality and integrity across multiple sensor streams.
• Debug firmware/software issues for reliable data acquisition.
• Execute and optimize compute workflows for metric analysis.
• Build tools and flows to evaluate model performance on large datasets.
• Generate hypotheses and interpret experiment results.
• Leverage data visualization to inform decisions.
• Prepare devices and setups for scheduled studies.
• Support operators; triage and debug issues in real time.
• Run validation checks across multi-sensor streams and fix anomalies.
• Organize data for analysis and benchmarking.
• Create visual summaries of data quality and outcomes.

Skills

Python

SQL

Data visualization

NumPy/SciPy/Pandas

Matplotlib/Seaborn

Signal processing

Firmware debugging

Javascript/Typescript

Large datasets

Data collection infra

Hardware sensors

Diagnostics across interfaces

Job description

Job Responsibilities:

• Design, plan, and execute data collection studies, working closely with research and product teams to gather high-quality biosensor data.
• Monitor and maintain the quality and integrity of large datasets, implementing robust data validation, cleaning, and quality custom/specialized monitoring processes for multiple sensor streams (IMU, EMG, optical sensors, etc.).
• Debug and troubleshoot firmware and software issues related to data collection, ensuring reliable data acquisition from hardware sensors.
• Execute, debug, and optimize distributed compute workflows for metric computation and analysis across large datasets.
• Build, modify, and maintain custom/specialized benchmarking tools, frameworks, and flows for evaluating and comparing model performance on large datasets.
• Generate and test hypotheses and analyze and interpret the results of data collection experiments.
• Leverage data visualization to help the team make decisions.
• Prepare devices and data collection setups for scheduled studies.
• Support test operators during data collection sessions; triage and debug issues in real time.
• Run validation/quality checks across multi-sensor streams, flag anomalies, and drive fixes.
• Process and organize newly collected data; maintain datasets for analysis and benchmarking.
• Execute and troubleshoot compute workflows for metric generation and reporting.
• Create visualizations and summaries to communicate data quality and experiment outcomes.

Skills:

• Experience with hardware sensors, data collection infrastructure, and data analysis pertaining to real-world human data.
• Experience with signal processing pertaining to time domain signals and/or medical imaging systems.
• Experience working with and managing large, high-dimensional datasets.
• 2+ years of experience performing data extraction, manipulation, and visualization using Python and SQL.
• Strong debugging skills, particularly related to data acquisition and firmware/software interfaces.
• Experience with Javascript/Typescript for front-end UI development.
• Experience with scientific computing and analysis packages such as NumPy, SciPy, Pandas.
• Experience with data visualization libraries such as Matplotlib, Pyplot, Seaborn, ggplot2.
• Coding Experience (Python required; SQL strongly preferred).
• Some hardware data experience. Sensor data collection Experience with hardware/sensor data and data collection (e.g., IMU/EMG/optical, or similar).
• Debugging/troubleshooting ability across software and hardware interfaces, with a focus on data quality and reliability.
• Additional years/depth of engineering experience beyond the minimum level.
• Signal processing experience (especially time-domain signals such as EMG/IMU).
• Scientific computing experience and experience working with large, high-dimensional dataset.

Education/Experience:

• NA