Data Architect @Montra Electric

Skill Sets Required:

15+ years of experience in Data Architecture and Engineering
Deep understanding of structured and unstructured data ecosystems
Hands-on experience with ETL, ELT, stream processing, querying, and data modeling
Proficiency in tools and languages such as Java, Spark, Kafka, Airflow, and Python
Proficiency in SQL and MongoDB
Proven experience in building and maintaining data warehouses and data lakes
Experience in implementing data quality standards and performing exploratory data analysis (EDA)
Experience with any public cloud platform: GCP, AWS, or Azure
Strong knowledge of data governance, privacy, and compliance standards
Expertise in designing high-performance and cost-optimized data engineering pipelines leveraging Medallion Architecture
A strategic mindset with the ability to execute hands-on when needed
Good to Have:

Knowledge of GraphQL/Graph databases
Exposure to connected vehicle platforms
Experience in the EV or automotive domain
Knowledge of API development
Exposure to data mesh and data fabric concepts
Responsibilities:

Design scalable, secure, and high-performance data architectures across cloud and on-premise environments for our connected vehicle platform
Lead the end-to-end architecture of data pipelines and real-time streaming solutions using Kafka, Kinesis, Google Pub/Sub, Apache Flink, or Dataflow.
Establish best practices for tiered storage across hot, warm, and cold data using BigQuery, Redshift, S3/GCS, and Delta Lake.
Collaborate with cross-functional teams and business stakeholders to ensure data solutions meet analytical and operational requirements
Enable AI/ML readiness through clean, governed, and performant data pipelines for predictive models.
Evaluate and recommend tools and technologies to improve the data platform by conducting proof of concepts (PoCs)
Ensure compliance with data security, privacy, and regulatory standards
Mentor and guide data engineers and solution architects
Establish best practices and coding standards for data engineering teams
Stay current with emerging technologies and drive adoption of modern data practices
Lead and contribute to technical discussions and decision-making

Qualifications:

Bachelor’s or Master’s degree in any discipline
15+ years of industry experience, with a minimum of 10 years in data engineering