data engineer for enterprise analytics

Stellenbeschreibung:

Описание:

Tesla develops electric vehicles, renewable energy products, battery systems, charging infrastructure, autonomous driving technology, and energy grid solutions. The company operates an enterprise analytics platform supporting business intelligence, manufacturing, supply chain, service, and energy operations under strict SOX compliance and change management controls.

Задачи:

Architect, build, and maintain Enterprise Data Warehouse and Lakehouse solutions for batch and near-real-time analytics
Design and implement ETL and ELT pipelines using Python and Apache Airflow or modern orchestration equivalents
Develop and operate real-time data streaming and processing platforms using Apache Kafka, Apache Spark Streaming, Structured Streaming, Flink, or equivalent technologies
Maintain the health of Vertica, SQL Server, Airflow, Tableau, and related platforms
Handle sensitive financial, production, and customer data systems while adhering to SOX controls, segregation of duties, change management, and audit requirements
Partner with business sponsors, product managers, manufacturing engineers, service operations, finance, and IT/security teams to gather requirements, scope projects, and deliver solutions
Communicate complex technical concepts and business impact through documentation, discussions, architecture diagrams, and executive-level presentations
Define, enforce, and continuously improve engineering standards, coding practices, testing methodologies, CI/CD patterns, monitoring and alerting, and quality assurance processes
Participate in design reviews, code walkthroughs, and pull request reviews across the team
Stay current with evolving open-source technologies and recommend adoption when they provide meaningful differentiation or operational efficiency

Требования:

Extensive professional experience as a Data Engineer, Backend Engineer, or ETL developer building large-scale data platforms
Strong SQL and Python skills for data engineering, including pandas, PySpark, SQLAlchemy, and API Scraping
Strong proficiency with Vertica, MySQL, SQL Server, NoSQL, OpenSearch, or similar database systems
Deep hands-on experience designing and operating Airflow DAGs in production at scale
Production experience with at least one distributed streaming system, such as Kafka, Kafka Streams, Spark Streaming, Flink, or Pulsar
Solid understanding of data modeling for analytical workloads
Experience building and operating systems under SOX compliance or similarly regulated environments
Understanding of distributed query engines
Experience with Docker and Kubernetes or ECS
Excellent communication skills and ability to explain technical trade-offs to engineers and business value to non-technical stakeholders