Who We Are
Solera
is a global leader in data and software services that strives to transform every touchpoint of the vehicle lifecycle into a connected digital experience. In addition, we provide products and services to protect life’s other most important assets: our homes and digital identities. Today, Solera processes over 300 million digital transactions annually for approximately 235,000 partners and customers in more than 90 countries. Our 6,500 team members foster an uncommon, innovative culture and are dedicated to successfully bringing the future to bear today through cognitive answers, insights,
algorithms
and automation. For more information, please visit
solera.com
.
The Role:
The Principal Data Engineer
is responsible for
designing, building, and
optimizing
data pipelines and platforms that enable scalable, reliable, and high-performance data processing. This role involves developing end-to-end data solutions using
cutting-edge
technologies while ensuring efficient ETL/ELT workflows to support analytics, reporting, and data-driven decision-making. The engineer will
analyze
complex data requirements, model data structures, and implement data integration solutions across diverse systems. Strong
expertise
in Streaming, performance tuning, and big data frameworks is essential, along with the ability to document, automate, and operationalize data pipelines for production use. The position also requires close collaboration with cross-functional teams — including data analysts, data scientists, and application developers — to ensure data quality, consistency, and availability. The Lead Data Engineer is expected to apply best practices in data engineering, cloud optimization, and governance, contributing to the continuous improvement and modernization of the data ecosystem.
ESSENTIAL RESPONSIBILITIES AND DUTIES:
Translates business requirements to conceptual solution architecture and high-level project estimates.
Develops,
modifies
, and implements Software as a Service (SaaS) hosted applications according to business requirements using.
Logical data
modeling
, database definition and manipulation, and data synchronization
Object oriented design, coding, performance tuning, and unit testing
Research, extraction and analysis of complex data
Authors and reviews technical requirements to ensure compliance with business requirements
Performs proper unit testing and software code writing (including automated unit testing)
Familiar with
appropriate standards
and techniques used during the Software Development Life Cycle process (SDLC, security) and applies them appropriately
Participates in the testing process through test review and analysis, test
witnessing
and certification of software
Participates in peer code reviews and develops skill level of others through mentorship
May work on multiple tasks/projects simultaneously with various team members and/or other groups both internally and externally
Engages effectively in self-directed time management and prioritization of workload
Perform such other duties as may be assigned by management
Essential to be available to work during Omnitracs corporate business hours
REQUIREMENTS:
Bachelor's degree in Computer Science, Engineering, or related technical field (Master's preferred)
10+ years of experience in data engineering roles
2+ years of experience in a technical leadership position
Utilizing Real-Time, Batch, and NoSQL/SQL technologies.
Proficiency in data warehousing, data modelling, ETL design, and optimization
Deep understanding of data enrichment, transformation, security, movement, data architecture, golden record, and data integrity.
Experience with the big data technologies (e.g., Hadoop, Spark, Kafka, Trino, Hive, Iceberg)
Experience with any data orchestration tools (Airflow, Luigi, etc.).
Familiarity with AWS migration services like Database Migration Service (DMS) and Server Migration Service (SMS).
Technical Skills
Programming Languages: Python, SQL, Java
Big Data Technologies: Apache Spark, Hadoop, Kafka, Hive, Trino
Any Cloud Platforms(AWS preferred):
AWS (S3, EC2, RDS, Lambda, DynamoDB, Glue)
Azure (Data Factory, Synapse, Databricks)
GCP (Big Query, Dataflow, Dataproc)
Data Warehousing: Snowflake, Redshift, Big Query, Fabric Data Warehouse
Any ETL/ELT Tools: Airflow, dbt, Informatica, Talend, Glue
Databases:
Relational: PostgreSQL, MySQL, SQL Server, Oracle
NoSQL: MongoDB, Cassandra, DynamoDB
Time-series: Influx DB, Timescale DB
Container & Orchestration: Docker, Kubernetes
Infrastructure as Code: Terraform, CloudFormation
Version Control: Git, GitLab/GitHub workflows
CI/CD: Jenkins, GitHub Actions, GitLab CI
Monitoring & Observability: Datadog, Grafana
Data Architecture Skills
Data modeling (dimensional, relational, NoSQL)
Database design and optimization
Batch and real-time pipeline architecture
Data mesh/data fabric architectures
Data lake and Lakehouse implementation
Understanding of API design and development and Microservices architecture is a plus
