Data Acquisition Engineer

Responsibilities:

• Design and execute data acquisition setups for industrial tests involving vibration, temperature, pressure, torque, strain, flow, electrical variables, and other process signals.
• Configure and operate high-fidelity DAQ systems (e.g., HBK/HBM, Dewesoft, Siemens, NI), including channel setup, sampling rates, filtering, synchronization, and triggering.
• Select, install, and validate industrial and scientific sensors, ensuring proper mounting, calibration, grounding, shielding, and overall signal integrity.
• Develop and maintain Python-based tools and scripts for data acquisition, automation, preprocessing, signal validation, data ingestion into internal pipelines, and exploratory analysis.
• Work closely with mechanical and automation teams to ensure test benches are properly instrumented, electrically integrated, and acquisition-ready.
• Support experimental planning, defining acquisition parameters, sensor requirements, sampling frequencies, and test methodologies to guarantee repeatability and statistical reliability.
• Ensure data quality, traceability, and comprehensive documentation of test configurations, wiring diagrams, sensor setups, DAQ settings, calibration routines, test logs, and acquisition parameters.
• Interface with data science teams to deliver clean, well-structured datasets suitable for AI model training and evaluation.
• Troubleshoot issues related to noise, grounding, synchronization, signal loss, connector faults, DAQ configuration errors, or hardware limitations.
• Continuously improve acquisition workflows, tooling, and laboratory best practices.

Requirements:

• Degree in Electrical Engineering, Electronics Engineering, Mechatronics, Physics, Computer Engineering, or a related field. (Candidates with equivalent experience in data acquisition or experimental testing environments are also encouraged to apply.)
• Advanced English.
• Availability to work on-site in São Paulo (Chácara Santo Antonio - Zona Sul)
• Hands-on experience with data acquisition systems and laboratory instrumentation in industrial, automotive, aerospace, or experimental test environments.
• Familiarity with DAQ hardware and software platforms, such as HBK/HBM, Dewesoft, Siemens, and NI (National Instruments).
• Strong understanding of sensor technologies, signal conditioning, sampling theory, noise mitigation, grounding, shielding, and measurement uncertainty.
• Proficiency in Python, especially for acquisition automation, data preprocessing, signal validation, and exploratory analysis.
• Experience working with time-series data, synchronous multisensor setups, and high-frequency sampling.
• Ability to operate in experimental or laboratory environments, following structured test procedures and ensuring data repeatability.
• Strong problem-solving and troubleshooting skills, particularly related to instrumentation, signal integrity, and DAQ configuration.
• Solid documentation habits, including versioning of test configurations, wiring diagrams, calibration routines, and acquisition parameters.
• Effective communication skills for collaborating with mechanical, automation, and data science teams, and for clearly reporting test results or technical findings.

Bonus Points:

• Experience working in automotive, aerospace, industrial testing, or NVH laboratories, where high-precision data acquisition and structured experimentation are essential.
• Background in vibration analysis, signal processing, or frequency-domain techniques (FFT, filtering, spectral analysis).
• Familiarity with strain gauge measurements, Wheatstone bridges, instrumentation amplifiers, and calibration procedures.
• Experience with MATLAB, LabVIEW, or other engineering-oriented environments for signal processing, automation, or measurement workflows.
• Knowledge of synchronization techniques across distributed DAQ systems, including PTP, GPS timing, or hardware triggering.
• Exposure to machine learning workflows, including dataset preparation, feature extraction, or anomaly detection using sensor data.
• Experience handling large experimental datasets, including structuring, labeling, cleaning, and managing test campaigns.
• Hands-on experience designing or building custom test fixtures, sensor mounts, or mechanical adaptations to support acquisition requirements.
• Experience managing and working with databases, including data modeling, storage, querying, and maintenance of large time-series datasets (e.g., SQL databases, time-series databases, or similar), supporting traceability, scalability, and long-term use of laboratory data.