Senior Data Platform & AI Engineering Leader

Lucid Motors

Phoenix (AZ)

On-site

USD 211,000 - 310,000

Full time

5 days ago

Be an early applicant

Application generator

Turn this role into an interview — a resume and cover letter built around what this employer wants.

Get past ATS filters

Benefits offered by this job

Medical, dental, and vision insurance

Paid time off

Equity program

401(k) retirement plan

Job summary

Lucid Motors is seeking a Senior Engineering Manager, Data Platform and AI to lead strategy, architecture, delivery, and operation of an enterprise data platform ingesting vehicle telemetry and enterprise data. You will oversee teams delivering data products and AI-ready foundations across engineering, manufacturing, supply chain, finance, and more.

You will champion data quality, governance, and scalable pipelines on AWS with Redshift and related tools, shaping data contracts and reusable data

Qualifications

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
12+ years of experience in data engineering, data platform engineering, distributed systems, or large-scale data technologies.
6+ years of experience leading engineering teams and delivering enterprise-scale, production data solutions.
Deep hands-on expertise in Amazon Redshift, including dimensional modeling, distribution and sort strategies, workload management, query and storage optimization, security, scaling, and cost management.
Proven experience integrating high-volume vehicle, IoT, connected-product, or telemetry data with enterprise transactional and master data.
Strong knowledge of data quality management, including profiling, validation, reconciliation, lineage, observability, quality metrics, and remediation processes.
Experience creating curated, governed, reusable data products and AI-ready datasets for analytics, machine learning, and Generative AI.
Deep understanding of batch and streaming architectures, schema evolution, event processing, time-series data, data contracts, and distributed data systems.
Strong AWS experience and hands-on knowledge of technologies such as S3, Glue, Lambda, Kinesis or Kafka, Airflow, Spark, IAM, Terraform, CI/CD, and observability platforms.
Strong programming skills in Python and SQL, with experience in Java and/or Scala preferred.
Strong software engineering foundation and experience operating secure, reliable, petabyte-scale data platforms in production.
Excellent communication and stakeholder management skills, with the ability to translate business and AI use cases into scalable technical solutions.

Responsibilities

Define and execute the strategy and roadmap for enterprise data, telemetry, and AI platforms.
Lead, mentor, and grow a high-performing team of data and platform engineers.
Drive engineering excellence through automation, operational rigor, and modern development practices.
Partner with business and technology leaders to deliver high-impact data products and capabilities.
Own the architecture, reliability, performance, security, and cost efficiency of the enterprise data platform on AWS.
Lead the design and operation of Amazon Redshift, including data modeling, distribution and sort strategies, workload management, scaling, performance tuning, access controls, and cost optimization.
Develop and operate scalable data lakes, warehouses, streaming platforms, orchestration frameworks, and self-service data capabilities.
Establish reusable platform patterns, data contracts, metadata standards, and automation that improve delivery speed and operational consistency.
Build and operate high-volume, resilient batch and real-time pipelines that ingest vehicle telemetry and data from enterprise applications.
Integrate and stitch telemetry with manufacturing, supply chain, sales, service, finance, customer, and master data to create consistent cross-domain views.
Address schema evolution, late-arriving events, time-series processing, event ordering, data contracts, and vehicle-to-enterprise identity resolution.
Curate governed, reusable data products and standardized business entities that support analytics, engineering diagnostics, machine learning, and Generative AI.
Provide secure, reliable self-service access to trusted telemetry and enterprise data for engineering, data science, and business teams.
Define and enforce data quality standards, ownership, controls, and measurable SLAs across critical data products.
Implement data profiling, validation, reconciliation, lineage, observability, anomaly detection, and root-cause remediation capabilities.
Establish monitoring, incident management, disaster recovery, and service-level objectives for business-critical data services.
Lead platform modernization, performance optimization, security, compliance, and cloud cost optimization initiatives.