Analytics Engineer, Mid-level

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Experian

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1996

💼 Consulting

📣 Marketing

📦 Logistics

Consulting • Marketing • Logistics

Experian is a global leader in digital experience, technology, and transformation. They partner with recognized brands to enhance customer understanding, innovate product strategies, and implement agile technology solutions. With a focus on delivering superior customer experiences through AI, cloud architecture, and project management, Experian helps businesses streamline their operations and achieve their objectives effectively.

📋 Description

• Build, evolve and maintain data products and features that support statistical models and analytical applications, translating raw data into reliable, reusable assets. • Develop and maintain scalable data pipelines (batch and/or streaming), ensuring efficiency, reliability and data quality throughout the lifecycle. • Work on analytical data modeling, including bronze, silver and gold layers. • Ensure governance, quality and observability of data products and features, including tests, monitoring, SLAs and alerts. • Implement and evolve processes for moving data between analytical and production environments, ensuring consistency and traceability. • Collaborate with Product, Engineering, Infrastructure, Security and Governance teams, acting as a bridge between technical and business areas. • Propose continuous improvements to data platforms, pipelines and architecture. • Develop reports and dashboards when necessary, with a focus on enabling product and technology decisions. • Ensure clear documentation and standardization of processes, datasets and features.

🎯 Requirements

• Strong proficiency in SQL and Python for data transformation and analysis. • Experience with distributed processing (Spark) and platforms such as Databricks. • Experience building and maintaining data pipelines using Airflow or similar orchestration tools. • Experience with version control (Git) and software engineering best practices. • Knowledge of CI/CD applied to data pipelines. • Experience in analytical data modeling, including dimensional modeling, data marts and bronze/silver/gold layers. • Experience with data testing and quality, including validation and monitoring. • Knowledge of cloud environments and data services, such as AWS, Azure or equivalents. • Ability to communicate clearly with both technical and business audiences. • Differentials: experience with feature platforms/feature stores; knowledge of Data Contracts and modern data governance; experience with Kubernetes or distributed architectures; experience with DataOps and/or data observability; familiarity with statistical modeling and machine learning concepts.

🏖️ Benefits

• Remote work option • Inclusive environment and a culture that supports balancing career with personal commitments and interests • Focus on well-being • Outstanding career and development opportunities • Recognized by Great Place To Work™ in 24 countries • International Top Employers certification