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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.

Job Overview

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.

Essential Functions

•    Data Platform Architecture: Design and evolve scalable, cloud-native data architectures on AWS, including data lakes, lakehouses, 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 CloudWatch, 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, NoSQL, 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 automated data validation, reconciliation, lineage, monitoring, alerting, and incident-response practices to ensure data completeness, accuracy, timeliness, and reliability.

•    Security and Governance: Partner with security and compliance teams to implement least-privilege access, encryption, secrets management, auditability, data retention, privacy controls, and appropriate handling of sensitive and regulated data.

•    DevOps and Infrastructure as Code: Build and support CI/CD pipelines and infrastructure automation using tools such as Terraform, AWS CloudFormation, or AWS CDK, enabling repeatable and reliable deployments across development, test, and production environments.

•    Performance and Cost Optimization: Evaluate and optimize pipeline performance, storage design, cloud consumption, and service utilization, implementing improvements that increase throughput, reliability, and efficiency while controlling AWS cost.

•    Data Science Enablement: Develop and provide curated, trusted, and well-documented datasets and feature-ready pipelines that support machine learning, model development, experimentation, production scoring, and post-deployment monitoring.

•    Cross-Functional Collaboration: Collaborate with product managers, software engineers, data scientists, analysts, and business stakeholders to define data contracts, service-level expectations, technical roadmaps, and delivery plans.

•    Technical Leadership: Lead technical and design reviews, establish data engineering standards and best practices, mentor engineers, and provide technical guidance for complex implementation and production-support issues.

•    Improvement: Evaluate emerging cloud, database, streaming, and data platform technologies and recommend practical improvements to MOTER’s architecture and engineering practices.

•    Perform other duties and responsibilities as assigned.

Qualifications

Required

•    Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Information Systems, or a related technical field; Master’s degree preferred.

•    7+ years of professional experience in data engineering, backend engineering, or large-scale data platform development, including ownership of production systems.

•    Deep hands-on experience designing, building, and operating data platforms on AWS.

•    Strong proficiency in Python, Java, and SQL, including development of production-quality, testable code.

•    Proven experience building and supporting ETL/ELT pipelines for large-scale structured, semi-structured, and unstructured datasets.

•    Strong experience with distributed data processing technologies such as Apache Spark and with orchestration frameworks such as Apache Airflow, AWS Step Functions, or AWS Managed Workflows for Apache Airflow.

•    Experience with relational databases and cloud data warehouses such as PostgreSQL, MySQL, Amazon Aurora, Amazon Redshift, or equivalent platforms.

•    Experience with NoSQL and event-oriented technologies such as DynamoDB, document databases, time-series databases, Kafka, Amazon MSK, or Amazon Kinesis.

•    Strong understanding of data modeling, schema evolution, partitioning, columnar file formats, data lake design, metadata management, and query optimization.

•    Experience implementing data quality, monitoring, logging, alerting, lineage, and operational support for production data pipelines.

•    Experience with Git, automated testing, CI/CD, containers, and infrastructure as code.

•    Strong understanding of cloud security, IAM, encryption, network controls, data privacy, and secure handling of sensitive information.

•    Demonstrated ability to solve complex technical problems, communicate clearly with technical and business stakeholders, and deliver in a fast-paced, collaborative environment.

•    Legal authorization to work in the U.S. without sponsorship.

Preferred

•    Experience with Microsoft Azure data services, such as Azure Data Lake Storage, Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Event Hubs, or Microsoft Fabric.

•    Experience designing multi-cloud or cloud-portable data architectures.

•    Experience with Databricks, Delta Lake, Apache Iceberg, Apache Hudi, or other lakehouse technologies.

•    Experience with geospatial, telematics, IoT, connected vehicle, camera, or high-frequency sensor data.

•    Experience supporting machine learning platforms, feature stores, model training pipelines, or real-time model scoring.

•    Familiarity with API design, microservices, event-driven architecture, and data contracts.

•    Experience with Kubernetes, Docker, and cloud-native application deployment.

•    Knowledge of data governance, cataloging, master data management, and regulatory or insurance data environments.

•    AWS professional or specialty certification, or equivalent cloud/data engineering certification.

•    Experience mentoring engineers and leading technical design or architecture reviews.

Office Location

•    Torrance, CA - Hybrid (Tuesday and Thursday in office)

Degree of Travel

•    10%

Compensation

•    For California based hires, the annual salary range is $128,000 - $169,000/year depending on experience.

•    This is an exempt position.

Benefits of Working with Us

•    Comprehensive benefits package including medical, dental, and vision coverage; basic life and long-term disability insurance; Health Savings Account (HSA); Flexible Spending Account (FSA); generous paid time off and holiday pay; and a 401(k) with company match.

•    Complimentary catered office lunches.

•    Flexible office hours to support work-life balance.

•    A collaborative culture where your input directly impacts the product and company trajectory.

•    A health and wellness-focused work environment with team social events.

•    Business casual dress code.

EEO Statement

MOTER Technologies Inc. is an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.

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