• Define and evolve the data architecture and integrations, with a focus on cloud cost efficiency (FinOps). • Conduct technical analyses of the current environment (Azure), identifying inefficiencies, bottlenecks, and opportunities for optimization. • Lead the strategy to migrate ETL pipelines from Azure Data Factory to GCP, ensuring performance, scalability, and cost reduction. • Work directly on building and evolving data pipelines, supporting the team in technical decisions and implementations. • Perform root cause analysis (RCA) for performance, cost, and availability issues, proposing structural solutions. • Define standards for data ingestion, processing, and consumption, balancing cost, performance, and complexity. • Define the target data architecture focused on cost reduction and cloud resource optimization. • Ensure the correct selection of technologies and approaches (batch vs streaming, relational vs non-relational, caching, etc.). • Lead and provide technical support for migrating ETLs to GCP (e.g., Dataflow, Composer, BigQuery, etc.). • Work hands-on reviewing and optimizing pipelines, queries, and jobs. • Work closely with the engineering team to ensure consistent and sustainable technical execution. • Define and monitor efficiency indicators (cost per pipeline, resource consumption, performance). • Document architectural decisions and build a reusable knowledge base. • Support technical prioritization with Delivery and Product, considering financial and operational impact.

• Desenvolver, manter e evoluir pipelines de dados com foco em eficiência, escalabilidade e otimização de custos em cloud. • Atuar na migração de ETLs do Azure Data Factory para GCP, implementando pipelines modernos e performáticos. • Executar análises técnicas do ambiente atual, identificando gargalos, desperdícios de recursos e oportunidades de melhoria. • Apoiar na implementação de soluções para redução de custo (FinOps), incluindo otimização de jobs, queries e uso de infraestrutura. • Realizar análise de causa raiz (RCA) de falhas, lentidão ou alto consumo de recursos. • Trabalhar em conjunto com Arquitetura para garantir aderência aos padrões definidos. • Construir e manter pipelines de ingestão, transformação e disponibilização de dados. • Implementar fluxos de dados em GCP (Dataflow, BigQuery, Pub/Sub, Composer/Airflow, etc.). • Refatorar pipelines existentes visando melhoria de performance e redução de custo. • Monitorar e otimizar jobs, garantindo eficiência no consumo de recursos. • Atuar na migração de pipelines do Azure Data Factory para GCP, garantindo continuidade operacional. • Implementar boas práticas de versionamento, testes e qualidade de dados. • Apoiar na definição de estratégias de processamento (batch vs streaming, incremental vs full load). • Colaborar com times de backend e arquitetura na integração com APIs e sistemas consumidores.

• You’ll help design, build, and maintain data solutions that power reporting and decision-making across the business. • Building and maintaining data pipelines using Google Cloud Platform (BigQuery, Cloud Functions, Cloud Composer, Cloud Scheduler). • Cleaning, transforming, and organising data from multiple sources (APIs, spreadsheets, internal systems). • Automating ETL / ELT workflows to improve reliability and efficiency. • Writing Python (and some Bash) scripts to support data processing and internal tools. • Building and maintaining dashboards and KPI reports using Looker Studio (and supporting data visualisation needs). • Preparing datasets for simple predictive or forecasting use cases as the team evolves. • This is a hands-on role — you’ll be writing code, fixing issues, improving pipelines, and seeing your work used by real teams.

The Insights & Advisory team at Cox Automotive is seeking a highly skilled and forward-thinking Senior Data Engineer to design, build, and optimize data architecture and pipelines that power strategic decision-making across the enterprise. This role combines deep technical expertise with a strong focus on data quality and integrity, ensuring that data is accessible, trusted, and actionable for internal teams and automotive OEM clients. You'll play a critical role in shaping the data infrastructure roadmap, enabling advanced analytics through AI, machine learning, and big data technologies, while embedding rigorous testing and validation practices into every stage of the data lifecycle. Key Responsibilities: Data Architecture & Engineering - Design and implement robust data architectures supporting structured and unstructured data from internal and external sources. - Build and maintain scalable, secure data pipelines using cloud-based and distributed technologies. - Establish data structures and routing mechanisms based on business and technical requirements. - Ensure alignment with enterprise architecture standards and business goals Data Quality & Automated Testing - Develop and execute automated test cases to validate ETL workflows, data pipelines, and reporting logic. - Create regression test suites to ensure ongoing data integrity and system stability. - Monitor and troubleshoot data anomalies, proactively identifying root causes and implementing fixes. - Maintain high standards of data quality across all reporting and analytical outputs. Advanced Data Solutions - Develop tools and programming to cleanse, organize, and transform data using AI, ML, and big data techniques. - Automate manual data processes, transforming them into repeatable, scalable capabilities. - Collaborate on application development projects to evolve database architecture and design. Data Analysis & Reporting - Analyze current and historical performance data to identify trends, variances, and opportunities. - Support dashboard and reporting development using tools like Tableau, Power BI, or Domo. - Fulfill routine and ad-hoc reporting requests using accepted metrics and methodologies. Collaboration & Stakeholder Engagement - Partner with data consumers, project managers, and business stakeholders to define logical and physical database designs for analytics models. - Collaborate with internal and external data providers to validate data, provide feedback, and customize data feeds and mappings. - Communicate findings and test results clearly to both technical and non-technical audiences. Process Improvement & Innovation - Identify and implement improvements in internal data management and testing processes. - Influence the data infrastructure roadmap through technical leadership and innovation. - Contribute to the development of design standards and assurance processes for software, systems, and applications. Minimum Qualifications - Bachelor's degree in a related discipline and 4+ years of experience in data engineering or architecture. The right candidate could also have a different combination, such as a master's degree and 2 years' experience; a Ph.D. and up to 1 year of experience; or 16 years' experience in a related field - Proven experience designing and building data pipelines and architectures in cloud environments (e.g., AWS, Azure, Snowflake). - Strong programming skills in Python, Scala, or Java, and proficiency in SQL. - Experience with big data technologies (e.g., Spark, Kafka, Hadoop) and machine learning frameworks. - Familiarity with ETL processes, data modeling, and data warehousing concepts. - Experience with automated testing frameworks (e.g., PyTest, Selenium, dbt tests) and data validation techniques. - Excellent problem-solving skills and ability to communicate technical concepts to non-technical stakeholders. - Experience supporting cross-functional teams including Finance, Sales, and Product Development is a plus. Preferred Skills - Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) for data transformation and predictive modeling. - Familiarity with data orchestration tools such as Apache Airflow, dbt, or Dagster. - Hands-on experience with cloud-native data platforms (e.g., Snowflake, AWS Redshift, Azure Synapse). - Knowledge of data governance and metadata management best practices. - Experience integrating external data sources and APIs into enterprise data ecosystems. - Strong understanding of CI/CD pipelines and DevOps practices for data engineering. - Ability to work in Agile environments and contribute to sprint planning and backlog grooming. - Exposure to real-time data streaming technologies (e.g., Kafka, Kinesis) is a plus. Why Join Cox Automotive? At Cox Automotive, data is at the heart of every decision. As a Sr Data Engineer, you'll be part of a collaborative team that values innovation, precision, and impact. This role offers the opportunity to shape the future of data infrastructure and empower business leaders with trusted, high-quality data solutions. USD 101,500.00 - 169,100.00 per year Compensation: Compensation includes a base salary in the range of $101,500.00 - $169,100.00. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program. Benefits: The Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. Employees are also eligible for additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave. Application Deadline: 03/18/2026