Data Acquisition Engineer

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.

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 Morumbi - São Paulo.

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.

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.