3 days ago
Sofia, BulgariaStaff+
Responsibilities
- Lead the design and evolution of scalable capabilities for developing, calculating, testing, governing, deploying, and operating data attributes.
- Build reusable ML and data engineering frameworks, tooling, automation, APIs, services, and ETL pipelines for production use.
- Design end-to-end architectures and integration patterns across analytical environments, data platforms, bureau platforms, and production systems.
- Evaluate data sources and methodologies, develop new attributes, and explore synthetic data for experimentation and product development.
- Design data-asset onboarding, migration, data-quality, and legacy workload modernisation approaches while maintaining business continuity.
- Lead performance, scalability, reliability, architecture, and code reviews and resolve complex cross-platform and production issues.
- Establish engineering standards and reference architectures, conduct proof-of-concepts, and enable teams across multiple international markets.
- Mentor engineers and data scientists and help local Analytics and Engineering teams operate new capabilities independently.
Requirements
- Strong software engineering fundamentals, including object-oriented programming, data structures, algorithms, design patterns, testing, maintainable design, version control, code review, and software development lifecycle practices.
- Hands-on production-grade Python experience with Pandas and NumPy, beyond analytical prototypes or notebooks.
- Experience designing APIs and services for data or ML capabilities; FastAPI experience is desirable.
- Applied machine learning and statistical knowledge, including model evaluation, feature engineering, overfitting, cross-validation, and train/validation/test methodologies.
- Experience with large-scale data processing using Apache Spark, PySpark, or Spark SQL and with Databricks or a comparable data/ML platform.
- Understanding of Delta Lake, distributed data processing, data-quality controls, ML lifecycle practices, model or feature tracking, deployment, and MLOps.
- Practical experience with Amazon Web Services or Azure, including cloud compute, object storage, networking, identity and access management, security, and managed data services.
- Experience with relevant cloud services such as S3, EC2, Lambda, EKS, SageMaker, OpenSearch, managed Kafka services, Blob Storage, Virtual Machines, Microsoft Entra ID, Databricks, or Azure Data Factory.
- Experience designing CI/CD pipelines and infrastructure as code, with Jenkins and Terraform experience desirable.
- Hands-on knowledge of Docker, Kubernetes, Linux, container-based deployment, production troubleshooting, monitoring, logging, and operational practices.
- Ability to translate requirements into technical designs, evaluate architectural trade-offs, design reusable solutions, manage technical risks, and lead architecture reviews.
- Ability to review and improve engineering and data-science code for performance, maintainability, and production readiness while mentoring technical teams.
- Experience with Apigee, Okta or similar API-management and SSO/identity platforms, legacy workload migration, or platforms and tooling for Data Science and Analytics teams is desirable.
- Experience in regulated, data-intensive environments such as financial services is advantageous.
- Ability to communicate complex technical concepts and collaborate across engineering, analytics, product, and geographically distributed teams.
Benefits
- Career development pathways, learning and development programs, and unlimited access to online training courses, learning materials, and books.
- Life insurance, food vouchers, additional health insurance, dental coverage, monthly flex allowance, corporate discounts, marriage and childbirth/adoption allowance, Multisport card, Sharesave plan, employee assistance program, and birthday gift.
- 25 days of paid vacation, one additional birthday day off, and three additional paid days for participation in a social responsibility event.
- Flexible working hours and home-office options; the role is hybrid remote/in-office.
- Inclusive, purpose-driven workplace culture and equal-opportunity employment with accommodation support available.
Tech Stack
Apache KafkaApache SparkAWSAzureDatabricksDockerFastAPIJenkinsKubernetesLinuxNumPyPandasPythonTerraform
Categories
Data EngineeringML Engineering
About Experian
Experian builds credit reporting, identity and fraud prevention, and data analytics software that help lenders, insurers, healthcare providers, and marketers make decisions; it also offers consumer credit monitoring and scores. The company earns via data licensing, decisioning software, and subscription services, operates in 30+ countries, is headquartered in Costa Mesa, California, and is a FTSE 100 public company listed on the London Stock Exchange (EXPN); it is one of the three major U.S. credit bureaus.