Principal Data Architect

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

At Lucid, we set out to introduce the most captivating, luxury electric vehicles that elevate the human experience and transcend the perceived limitations of space, performance, and intelligence. Vehicles that are intuitive, liberating, and designed for the future of mobility.

We plan to lead in this new era of luxury electric by returning to the fundamentals of great design – where every decision we make is in service of the individual and environment. Because when you are no longer bound by convention, you are free to define your own experience.

Come work alongside some of the most accomplished minds in the industry. Beyond providing competitive salaries, we’re providing a community for innovators who want to make an immediate and significant impact. If you are driven to create a better, more sustainable future, then this is the right place for you.

Principal Data Architect is responsible to define and lead the Data Architecture, Data Quality, Data Governance at lucid, for both Vehicle and Operational data, ingesting, processing, and storing Trillions of rows of data per day. This hands-on role helps solve real big data problems, which most of the standard tools on the market are not capable of handling. You will be designing solutions, writing codes and automation, defining standards, and establish best practices across the company. 

Role

• Design, implement and lead Data Architecture, Data Quality, Data Governance across Lucid
• Defining data modeling standards and foundational best practices
• Develop and evangelize data quality standards and practices
• Establish data governance processes, procedures, policies, and guidelines to maintain the integrity and security of the data.
• Drive the successful adoption of organizational data utilization and self-serviced data platform. 
• Create and maintain critical data standards and metadata that allows data to be understood and leveraged as a shared asset
• Develop standards and write templet codes for sourcing, collecting, and transforming data for streaming or batch processing data.
• Design data schemes, object models, and flow diagrams to structure, store, process, and integrate data
• Provide architectural assessments, strategies, and roadmaps for data management.
• Apply hands-on subject matter expertise in the Architecture and administration of Big Data platforms, Data Lake Technologies (AWS S3/Hive), and experience with ML and Data Science platforms.
• Implement and manage industry best practice tools and processes such as Data Lake, Delta Lake, S3, Spark ETL, Airflow, Hive Catalog, Ranger, Redshift, Spline, Kafka, MQTT, Timeseries Database, Cassandra, Redis, Presto, Kubernetes, Docker, CI/CD, DevOps
• Translate big data and analytics requirements into data models that will operate at a large scale and high performance and guide the data analytics engineers on these data models.
• Define templates and processes for the design and analysis of data models, data flows, and integration.
• Lead and mentor Data Analytics team members in best practices, processes, and technologies in Data platforms

Qualifications

• BS. or MS in Computer Science, or equivalent.
• 15+ years of hands-on experience in Data Warehouse, ETL, Data Modeling & Reporting.
• 7+ years of hands-on experience in productionizing and deploying Big Data platforms and applications, Hands-on experience working with: Relational/SQL, distributed columnar data stores/NoSQL databases, time-series databases, Spark streaming, Kafka, Hive, Parquet, Avro, and more
• extensive experience in understanding a variety of complex business use cases and modeling the data in the data warehouse. 
• Highly skilled in SQL, Python, Spark, AWS S3, Hive Data Catalog, Parquet, Redshift, Airflow, and Tableau or similar tools.
• Proven experience in building a Custom Enterprise Data Warehouse or implementing tools like Data Catalogs, Spark, Ranger, Presto, Tableau, Kubernetes, and Docker
• Knowledge of infrastructure requirements such as Networking, Storage, and Hardware Optimization with Hands-on experience with Amazon Web Services (AWS)
• Strong verbal and written communications skills are a must and work effectively across internal and external organizations and virtual teams.
• Demonstrated industry leadership in the fields of Data Warehousing, Data Science, and Big Data related technologies.
• Strong understanding of distributed systems and container-based development using Docker and Kubernetes ecosystem 
• Deep knowledge of data structures and algorithms.
• Experience in working in large teams using CI/CD and agile methodologies.