15 hours ago
Sofia, BulgariaStaff+
Responsibilities
- Lead the design and evolution of capabilities for developing, calculating, testing, governing, deploying, and operating data attributes at scale.
- Build reusable frameworks, tooling, automation, APIs, and production-grade ML or data services.
- Design end-to-end architectures and integration patterns across analytical environments, data platforms, cloud services, and production systems.
- Evaluate data sources and methodologies, including synthetic data, and translate data assets into new attributes and analytical capabilities.
- Design data ingestion, transformation, ETL, data-quality, asset-onboarding, and legacy-migration approaches.
- Lead performance, scalability, reliability, architecture, code, and integration reviews and resolve complex technical issues.
- Provide hands-on technical leadership for implementations and proof-of-concepts across multiple markets.
- Establish reference architectures, engineering standards, development practices, and reusable implementation patterns.
- Mentor engineers and data scientists and enable distributed teams to adopt and operate new capabilities independently.
Requirements
- Strong software engineering fundamentals, including object-oriented programming, data structures, algorithms, design patterns, testing, maintainable design, code review, and version control.
- Hands-on Python experience with Pandas and NumPy and experience building production-grade data or ML applications.
- 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 Apache Spark or PySpark, Spark SQL, Databricks, Delta Lake, distributed data processing, data-quality controls, and reusable ML or analytical features.
- Experience with ML lifecycle, model or feature tracking, deployment, or MLOps practices is desirable.
- Practical experience with Amazon Web Services or Azure and knowledge of compute, object storage, networking, identity and access management, security, and managed data services.
- Experience with relevant AWS services such as S3, EC2, Lambda, EKS, SageMaker, OpenSearch, and managed Kafka services, or equivalent technologies.
- Azure experience with Blob Storage, Virtual Machines, networking, Microsoft Entra ID, Databricks, and Azure Data Factory is desirable.
- Experience with CI/CD pipelines, Terraform or other Infrastructure as Code, Docker, Kubernetes, Linux, production troubleshooting, monitoring, logging, and operational practices.
- Ability to translate requirements into technical designs, evaluate architectural trade-offs, establish engineering standards, and review code for performance, maintainability, and production readiness.
- Experience with Apigee, Okta or comparable SSO and identity integrations, legacy workload migration, or platforms and tooling for Data Science and Analytics teams is desirable.
- Ability to communicate complex technical concepts, collaborate across engineering, analytics, product, and distributed teams, and manage technical and delivery risks.
- Experience in regulated, data-intensive environments such as financial services is advantageous.
Benefits
- Career development pathways, learning and development programs, and unlimited access to online educational courses, learning materials, and books.
- Life insurance, food vouchers, additional health insurance, dental coverage, monthly flex allowance, corporate discounts, marriage and childbirth or adoption allowance, Multisport card, Sharesave plan, employee assistance program, and birthday gift.
- 25 days of paid vacation, one additional birthday day off, and three extra paid days for participation in a social responsibility event.
- Flexible working hours and home office; this is a hybrid remote/in-office role.
- Inclusive, purpose-driven work environment with recognized team spirit and recreational activities.
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.