3 days ago
Prague, CzechiaSenior
Responsibilities
- Design, develop, and maintain scalable data ingestion, transformation, enrichment, ETL, ELT, and integration pipelines.
- Modernize and migrate legacy applications, databases, file systems, repositories, operational workflows, and on-premise data platforms to scalable, governed, cloud-enabled architectures.
- Build data processing solutions using Python, Spark, SQL, and distributed computing frameworks.
- Design data architectures and AI-ready datasets supporting LLM applications, retrieval-augmented generation, semantic search, knowledge management, feature stores, training data, and inference pipelines.
- Develop metadata, lineage, cataloging, data quality, validation, monitoring, and observability solutions.
- Support Data-as-a-Service platforms, reusable data products, AI platform engineering, model-serving infrastructure, feature management, and AI data lifecycle management.
- Implement data privacy, security, access control, encryption, governance, responsible AI, and enterprise compliance practices.
- Collaborate with data engineering, machine learning, data science, product, architecture, and business stakeholders on automation, technical debt reduction, and platform modernization.
- Participate in architecture reviews, solution design, sprint planning, and continuous improvement initiatives.
Requirements
- Bachelor's or master's degree in Computer Science, Data Science, or a related field.
- At least 5 years of experience as a Big Data Engineer or similar.
- At least 5 years of experience migrating legacy data platforms, repositories, and operational workflows to modern data architectures while maintaining business continuity and data integrity.
- Strong proficiency in Scala, Python, or Java, with experience building solutions using Python, SQL, Spark, and distributed computing frameworks.
- Familiarity with APIs, data integration frameworks, event-driven architectures, modern data architectures, data lakes, lakehouses, and data mesh concepts.
- Understanding of AI and machine learning workflows, including training data preparation, feature engineering, and model-serving data pipelines.
- Knowledge of data security, encryption, access controls, compliance requirements, data governance, lineage, and responsible AI practices.
- Experience with Git, CI/CD pipelines, and infrastructure-as-code practices.
- Strong analytical, problem-solving, communication, stakeholder management, organizational, prioritization, and collaboration skills.
- Ability to identify automation and modernization opportunities, manage multiple projects, learn new technologies quickly, and work effectively in fast-paced, multicultural, international environments.
Tech Stack
Categories
Data Engineering
About Solera
Solera provides SaaS platforms, data, and services spanning the vehicle lifecycle—claims, repairs, dealer operations, telematics, and fleet management—for insurers, repairers, dealerships, and fleets. Its portfolio includes Audatex, DealerSocket, Omnitracs, LoJack, and Spireon, and it processes over 300 million transactions annually for about 235,000 customers in 90+ countries. Founded in 2005 and headquartered in Westlake, Texas, Solera is privately held under Vista Equity Partners.