Company
Technology
Senior/LeadDataEngineer–AI-NativeAftermarketPlatform
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior/Lead Data Engineer – AI-Native Aftermarket Platform. Skills: Data Engineering, AI Workloads, Data Modeling, Python Systems. Design scalable data pipelines. Build scalable data pipelines”
Industry & Context.
Performance troubleshooting; Resolve performance issues; Resolve pipeline issues
What They're Looking For.
Must Have
Expertise in SQL, Dimensional data modeling, Medallion architecture, SCD patterns, Dataset grain management, Extensive experience building production-grade data pipelines using Python, Testing practices (pytest), Typing, Code quality tools, Deep hands-on experience with Spark/PySpark, Performance troubleshooting using tools such as Spark UI, Experience with dbt, Model development, Testing frameworks, Data documentation practices, Solid knowledge of Databricks, Delta Lake, Modern lakehouse architectures, Experience working with cloud data platforms, GitHub, CI/CD workflows, Secret management systems, Proven ability to operate in complex, distributed systems, Make architectural trade-offs, Communication skills, Document technical decisions, Translate engineering work into business impact, Leadership experience mentoring engineers, Setting technical direction, Contributing to engineering best practices
Nice to Have
Familiarity with Azure ecosystem tools (ADF, ADLS), Modern observability tools, AI-assisted engineering tools
What You'll Do.
Design scalable data pipelines
Build scalable data pipelines
Maintain scalable data pipelines
Implement data quality frameworks
Enforce data quality frameworks
Develop Databricks-based workloads
Deploy Databricks-based workloads
Operate within secure cloud environments
Operate within governed cloud environments
Provide technical leadership
Define architecture standards
Guide cross-team engineering decisions
Ensure reliability of data systems
Ensure observability of data systems
Identify performance issues
Resolve performance issues
Identify pipeline issues
Resolve pipeline issues
How You'll Work.
Team & Collaboration
Cross-team engineering decisions
Communication Scope
Document technical decisions; Translate engineering work
Process & Methodology
CI/CD workflows
Full Job Description
## Accountabilities Design, build, and maintain scalable, idempotent end-to-end data pipelines using modern data stack principles to support analytics and AI workloads. Develop robust data models (star and snowflake schemas) and write high-quality, grain-aware SQL to build scalable and reliable data marts. Build production-grade Python systems with strong engineering discipline, including testing, type safety, and modular design. Develop and manage dbt models across layered architectures (staging, intermediate, marts), ensuring strong testing and documentation standards. Implement and enforce data quality frameworks, including validation checks, schema enforcement, and anomaly detection across pipelines. Develop and deploy Databricks-based workloads (including Asset Bundles) and operate within secure, governed cloud environments. Provide technical leadership by defining architecture standards, reviewing code, mentoring engineers, and guiding cross-team engineering decisions. Ensure reliability and observability of data systems, while proactively identifying and resolving performance or pipeline issues. Requirements: Strong expertise in SQL and dimensional data modeling, including medallion architecture, SCD patterns, and dataset grain management. Extensive experience building production-grade data pipelines using Python, with strong testing practices (pytest), typing, and code quality tools. Deep hands-on experience with Spark/PySpark and performance troubleshooting using tools such as Spark UI. Strong experience with dbt, including model development, testing frameworks, and data documentation practices. Solid knowledge of Databricks, Delta Lake (MERGE, OPTIMIZE, Z-ORDER, time travel), and modern lakehouse architectures. Experience working with cloud data platforms and tools such as GitHub, CI/CD workflows, and secret management systems. Proven ability to operate in complex, distributed systems and make architectural trade-offs between cost, scalability, and perform
Applying for this Senior/Lead Data Engineer – AI-Native Aftermarket Platform role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Lever
- Lever uses a streamlined one-page form — apply in under 5 minutes.
- LinkedIn import works well; review parsed data before submitting.
- The cover letter field is optional but visible to reviewers — use it to differentiate.
- Referral codes from employees can significantly boost visibility of your application.
ANONYMOUS · UNFILTERED
What do employees actually say about this company?
Real rants from real employees. Read before you apply.