Data Engineer, ADAS Fleet Analytics

Mercedes-Benz Research & Development North America, Inc.

San Jose (CA)

On-site

USD 118,000 - 148,000

Full time

10 hours ago

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Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401(k) with employer match

Up to 15 holidays

Paid time off

Tuition assistance

Wellness reimbursement

Job summary

Mercedes-Benz Research and Development North America, Inc. is seeking a Data Engineer to build and operate data pipelines turning raw vehicle telemetry into trusted KPIs and dashboards.

You will manage full data lifecycles from real-time ingestion to model-ready data, with a focus on Bronze/Silver/Gold processing using PySpark and Delta Lake on Azure. The role includes VIN enrollment, fleet-health tracking, coverage-gap detection, and data campaigns end-to-end in a modern, AI-enabled data

Qualifications

Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field; equivalent experience may be considered.
3+ years of professional experience.
2-5 years of experience in data engineering or big-data analytics with production pipeline ownership.
Strong Python, PySpark DataFrame API and performance-tuning skills, and SQL skills including window functions and aggregations.
Experience with Delta Lake, Iceberg, Hudi, or Parquet in data lake environments.
Experience with automated testing for data pipelines and Git-based development workflows.
Ability to diagnose production failures and communicate trade-offs to technical and non-technical stakeholders.

Responsibilities

Design and maintain scalable telemetry pipelines using Event Hub, Medallion architecture, Delta Lake MERGE, schema evolution, and blue/green data deployments.
Own campaign and fleet management, including enrollment rosters, ingestion-completeness monitoring, VIN reconciliation, and automated coverage alerts.
Develop fleet KPIs for engineering, including safety events and takeover analysis; operations, including data freshness and pipeline health; and management, including trends and cohort comparisons.
Build ML-ready data infrastructure, including video and signal pipelines for SSR recordings, embedding stores, feature-engineering layers, and model-output integration into Gold.
Operate the Azure data platform, including ADLS Gen2, Synapse Spark, Event Hub, and Container Apps, and support Terraform-managed infrastructure and CI/CD.
Write automated tests, maintain documentation alongside code, and participate in code reviews and incident response.

Skills

Python

PySpark

SQL

Delta Lake

Data pipelines

Azure/AWS/GCP

Data governance

Education

Bachelor's or Master's in CS/Data Engineering

Tools

Delta Lake

Iceberg

Hudi

Parquet

Job description

Parquet schema design and schema evolution.

Aufgaben

Position Overview

Mercedes-Benz uses large-scale vehicle telemetry to evaluate, monitor, and improve its Advanced Driver Assistance Systems. As a Data Engineer on the US ADAS Data & Forensics team, you will build and operate the data pipelines that turn raw vehicle signals into trusted KPIs, fleet dashboards, and campaign-health monitoring for management and the engineers who calibrate ADAS functions. You will own the full data lifecycle, including real-time ingestion, binary protocol decoding, Bronze/Silver/Gold processing with PySpark and Delta Lake, and delivery through APIs and dashboards. You will also manage data campaigns end to end, including VIN enrollment, fleet-health tracking, coverage-gap detection, and the Sold to Consented to Capable to Active vehicle funnel. The architecture is evolving toward applied AI, and you will build the data infrastructure required for vision-language models, LLM-based event classification, embedding retrieval, and model-based event scoring. Relocation assistance (domestic or international) is not provided for this position.

Job Responsibilities

Design and maintain scalable telemetry pipelines using Event Hub, Medallion architecture, Delta Lake MERGE, schema evolution, and blue/green data deployments.
Own campaign and fleet management, including enrollment rosters, ingestion-completeness monitoring, VIN reconciliation, and automated coverage alerts.
Develop fleet KPIs for engineering, including safety events and takeover analysis; operations, including data freshness and pipeline health; and management, including trends and cohort comparisons.
Build ML-ready data infrastructure, including video and signal pipelines for SSR recordings, embedding stores, feature-engineering layers, and model-output integration into Gold.
Operate the Azure data platform, including ADLS Gen2, Synapse Spark, Event Hub, and Container Apps, and support Terraform-managed infrastructure and CI/CD.
Write automated tests, maintain documentation alongside code, and participate in code reviews and incident response.

Qualifikationen

Minimum Qualifications

Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field. Equivalent experience may be considered.
3+ years of professional experience.
2-5 years of experience in data engineering or big-data analytics with production pipeline ownership.
Strong Python, PySpark DataFrame API and performance-tuning skills, and SQL skills including window functions and aggregations.
Experience with a columnar or transactional data-lake technology such as Delta Lake, Iceberg, Hudi, or managed Parquet.
Experience with automated testing for data pipelines and Git-based development workflows.
Ability to diagnose production failures, including schema conflicts, data skew, and memory issues, and communicate trade-offs to technical and non-technical stakeholders.

Strongly Preferred

Medallion or other layered data-processing patterns.
Cloud data-platform experience in Azure, AWS, or GCP.
Parquet schema design and schema evolution.
Production data-quality practices, including deduplication, idempotency, and data contracts.
Time-series, IoT, or vehicle-telemetry data.

Nice to Have

DuckDB or PyArrow; FastAPI or analytical API delivery; blue/green data deployments.
ML data infrastructure, including feature stores, embedding pipelines, vector databases, or video pipelines.
Fleet or campaign management for vehicle or IoT programs.
Automotive data privacy, including CCPA; ADAS or connected-vehicle domain knowledge.
Azure Data Engineer or Databricks certification.
English required; German is an advantage.

The successful candidate’s starting pay will be determined based on a wide range of factors, including, but not limited to, job-related education, skills, and experience, geographic location, and market conditions. The current salary range for this position is as follows and may be modified in the future: $118,200 - $147,700.

This position may involve access to export-controlled technology, hardware, or software under the Export Administration Regulations (EAR), 15 C.F.R. Parts 730-774 and possibly technical data, defense articles and defense services subject to the International Traffic in Arms Regulations (ITAR), 22 C.F.R. Parts 120-130. As such, Mercedes-Benz Research & Development North America may be required to obtain an export license or authorization in accordance with United States law.

Why should you apply?

Here at MBRDNA, you create digital ecosystems around cars, you design a language between humans and machines, you make a car even more intelligent - you make the new reality for cars. MBRDNA was honored as one of the "Best Places to Work" by BuiltIn in January 2024, a testament to our commitment to creating an exceptional work environment. At each of our offices, we foster a culture of collaboration and continuous learning, ensuring every team member can thrive and innovate.

Benefits for Full-Time* Employees Include:

Medical, dental, and vision insurance for employees and their families
401(k) with employer match
Up to 15 company-paid holidays
Paid time off (flexible time off for salaried employees), sick time, and parental leave
Tuition assistance program
Wellness/Fitness reimbursement programs
Internships & Contractors excluded from Full-Time Employee benefits

Benefits