Data Diagnostics Engineer

PeoplePilot; Foster City (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

PeoplePilot is seeking a Data Diagnostics Engineer in Foster City, CA to develop and validate data-driven automotive diagnostics. You will leverage large-scale fleet and telemetry data to characterize faults, tune thresholds, and validate algorithms.

The role requires hands-on experience with UDS/ISO 14229, DTC management, CAN/LIN/Automotive Ethernet, and proficiency in Python and C++. Databricks experience is essential for processing big data in a fleet context.

Qualifications

• 3+ years of professional experience in automotive diagnostics.
• Hands-on experience with automotive diagnostic systems, fault detection, and DTC management.
• Working knowledge of UDS / ISO 14229.
• Experience with OBD-II / SAE J1979.
• Working knowledge of CAN, LIN, and Automotive Ethernet.
• Proficiency in Python for data analysis and visualization.
• Experience with large-scale telemetry or fleet datasets.
• Experience with Databricks or an equivalent large-scale data analytics platform.
• Proficiency or hands-on experience with C++.
• Experience with HIL/SIL testing frameworks.
• Experience validating diagnostic algorithms and analyzing test results.
• Understanding of diagnostic performance metrics including detection rate and false-positive rate.

Responsibilities

• Leverage large-scale fleet data in Databricks to characterize and analyze vehicle faults.
• Analyze telemetry and diagnostic datasets to identify fault patterns, trends, and anomalies.
• Tune diagnostic detection thresholds using real-world fleet data.
• Validate diagnostic algorithm performance based on detection rate, false-positive rate, and diagnostic accuracy.
• Investigate diagnostic failures, false positives, missed detections, and other fault-related issues.
• Support the development and validation of diagnostic monitoring strategies.
• Apply automotive diagnostic standards and protocols including UDS / ISO 14229, DTC Management, OBD-II, and SAE J1979.
• Work with automotive communication networks including CAN, LIN, and Automotive Ethernet.
• Use Python for data analysis, processing, automation, and visualization.
• Develop scripts and analytical tools to investigate diagnostic and telemetry data.
• Apply C++ knowledge to support diagnostic software and algorithm development.
• Collaborate with software and diagnostic engineering teams to improve diagnostic implementations.
• Work with HIL and SIL testing frameworks to validate diagnostic functionality.
• Support automated diagnostic testing and regression validation.
• Correlate HIL/SIL test results with real-world fleet and telemetry data.
• Drive continuous improvement of diagnostic algorithms using data-driven insights.

Skills

Databricks

Python

C++

HIL/SIL testing

UDS / ISO 14229

OBD-II / SAE J1979

CAN / LIN / Automotive Ethernet

DTC management

Telemetry data

Fleet data analysis

Tools

Databricks

HIL/SIL testing frameworks

Job description

Experience: 3+ years of professional experience in automotive diagnostics

Workplace: On-site

Employment Type: Full-time

About the Role

We are seeking a Data Diagnostics Engineer to support data-driven automotive diagnostic development and validation. This role will focus on leveraging large-scale fleet and telemetry data to characterize vehicle faults, tune diagnostic detection thresholds, validate algorithm performance, and drive continuous improvement.

The ideal candidate will bring hands-on experience in automotive diagnostics, UDS, CAN, Python, C++, and HIL/SIL testing, along with experience working with Databricks or equivalent large-scale telemetry data platforms.

Key Responsibilities

• Leverage large-scale fleet data in Databricks to characterize and analyze vehicle faults.
• Analyze telemetry and diagnostic datasets to identify fault patterns, trends, and anomalies.
• Tune diagnostic detection thresholds using real-world fleet data.
• Validate diagnostic algorithm performance based on detection rate, false-positive rate, and diagnostic accuracy.
• Investigate diagnostic failures, false positives, missed detections, and other fault-related issues.
• Support the development and validation of diagnostic monitoring strategies.
• Apply automotive diagnostic standards and protocols including UDS / ISO 14229, DTC Management, OBD-II, and SAE J1979.
• Work with automotive communication networks including CAN, LIN, and Automotive Ethernet.
• Use Python for data analysis, processing, automation, and visualization.
• Develop scripts and analytical tools to investigate diagnostic and telemetry data.
• Apply C++ knowledge to support diagnostic software and algorithm development.
• Collaborate with software and diagnostic engineering teams to improve diagnostic implementations.
• Work with HIL and SIL testing frameworks to validate diagnostic functionality.
• Support automated diagnostic testing and regression validation.
• Correlate HIL/SIL test results with real-world fleet and telemetry data.
• Drive continuous improvement of diagnostic algorithms using data-driven insights.

Key Requirements

• 3+ years of professional experience in automotive diagnostics.
• Hands-on experience with automotive diagnostic systems, fault detection, and DTC management.
• Working knowledge of UDS / ISO 14229.
• Experience with OBD-II / SAE J1979.
• Working knowledge of automotive communication buses including CAN, LIN, and Automotive Ethernet.
• Proficiency in Python for data analysis and visualization.
• Experience working with large-scale telemetry or fleet datasets.
• Experience with Databricks or an equivalent large-scale data analytics platform.
• Proficiency or hands-on experience with C++.
• Experience with HIL/SIL testing frameworks.
• Experience validating diagnostic algorithms and analyzing test results.
• Understanding of diagnostic performance metrics including detection rate and false-positive rate.

Core Technical Skills

Automotive Networks: CAN, LIN, Automotive Ethernet

Programming: Python, C++

Data & Analytics: Databricks, Fleet Data, Telemetry Data, Data Analysis, Data Visualization

Ideal Candidate Profile

The ideal candidate is an automotive diagnostics engineer with strong data-analysis capabilities. You should be comfortable working with diagnostic protocols and vehicle communication networks while using Python and large-scale fleet data to identify faults, tune diagnostic thresholds, and evaluate diagnostic algorithm performance.

Experience with Databricks, C++, and HIL/SIL validation will be particularly valuable in this role.

If you have a strong background in automotive diagnostics and enjoy using real-world vehicle data to improve diagnostic performance, we encourage you to apply.