Senior Data Engineer

Salary Range: $ To $ Annually

MOTER Technologies, Inc. (“MOTER”) is unlocking the world of connected vehicle data to power innovative products and services for the automotive, mobility, and insurance industries. Our edge-computing-based advanced data analytics and software platform transforms vehicle data into actionable insights that drive the next generation of driver scoring, risk analytics, and insurance solutions.

MOTER is modernizing the insurance industry by equipping insurers with real-time vehicle insights to enhance underwriting, streamline claims, and improve risk assessment. To drive this transformation, MOTER is building white-labeled insurance products for some of the most recognized brands in the automotive industry. These offerings leverage the full suite of MOTER’s connected vehicle data solutions—including next-generation telematics and claims visualization tools—bridging the gap between automakers and insurers, accelerating innovation, and delivering measurable value across the mobility ecosystem.

The Senior Data Engineer reports to the VP of Engineering and is responsible for designing, building, and operating the scalable data foundation that powers MOTER’s connected vehicle, insurance, risk analytics, and customer-facing products. The Senior Data Engineer will lead the development of cloud-native data platforms and pipelines that ingest, process, store, govern, and serve high-volume structured, semi-structured, and unstructured data from vehicles, telematics devices, cameras, APIs, business systems, and external data providers.

The Senior Data Engineer will bring deep expertise in Amazon Web Services (AWS), large-scale distributed data processing, data lake and warehouse architecture, and production software engineering using Python, Java, and SQL. The Senior Data Engineer will collaborate closely with data science, application engineering, edge systems, product, security, and business teams to deliver reliable, secure, observable, and cost-efficient data products.

The Senior Data Engineer will provide strong technical ownership and architectural judgment across data engineering initiatives and will help establish scalable, maintainable engineering practices. The role will also mentor other engineers and translate complex business, product, and analytical requirements into well-designed, maintainable production solutions.

• Data Platform Architecture:
  Design and evolve scalable, cloud-native data architectures on AWS, including data lakes, lake houses, data warehouses, operational data stores, and analytical serving layers.
• Data Pipeline Engineering:
  Build and maintain reliable batch, micro-batch, and real-time data ingestion and transformation pipelines for high-volume vehicles, telematics, sensor, image/video metadata, insurance, and business data.
• AWS Engineering:
  Implement and optimize solutions using services such as Amazon S3, AWS Glue, Amazon EMR, Amazon Redshift, Amazon Athena, AWS Lambda, Amazon Kinesis, Amazon MSK, Amazon RDS/Aurora, DynamoDB, Step Functions, ECS/EKS, Lake Formation, IAM, and Cloud Watch, selecting services based on scalability, latency, security, reliability, and cost requirements.
• Large-Scale Processing:
  Develop and optimize distributed processing workloads using Apache Spark and related big-data technologies; improve partitioning, file formats, query performance, cluster utilization, and compute cost.
• Structured and Unstructured Data:
  Design storage and access patterns for relational, No
  
  SQL, time-series, geospatial, document, event-stream, and unstructured data, including schemas, metadata, indexing, cataloging, and lifecycle policies.
• Data Modeling:
  Create and maintain conceptual, logical, and physical data models for analytical and operational use cases, including dimensional, normalized, denormalized, and event-driven models.
• Software Engineering:
  Develop reusable, testable, and production-quality data services, libraries, APIs, and automation using Python, Java, and SQL, while applying standards for code review, version control, testing, and documentation and maintainability.
• Data Quality and Observability:
  Implement…