Job detail for Data Engineering Specialist I

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Data Engineering Specialist I
Experian
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Company Description

Experian is a global data and technology company that powers opportunities for people and businesses around the world. We operate in diverse markets, such as financial services, healthcare, automotive, agribusiness, insurance, and others. Experian invests in people and new advanced technologies to unlock the power of data. We have an incredible team of 25,200 employees in 32 countries.

Our uniqueness is that we celebrate yours. Experian's people-first, inclusive, and purpose-driven culture is recognized by numerous awards—including World’s Best Workplaces™ 2025 (Fortune Global Top 25) and Great Place To Work™ in 26 countries, among others. Check out Experian Life on social media or explore our careers site to understand why. Experian is also proud to be an equal opportunity and affirmative action employer. If you have a disability or need that requires accommodation, we ask that you let us know as soon as possible.

Job Description

Responsible for transforming analytical models into reliable, observable, and scalable systems, from feature pipelines to production monitoring. You will act as a technical reference within the team, working in close partnership with Data Scientists, Data Engineers, Architecture, and Product teams, helping to raise the company's ML engineering standards.

Key :

  • Design, build, and maintain end-to-end ML pipelines (training, validation, deployment, and retraining) with automation and reproducibility.

  • Deploy models into production at scale, in batch, near-real-time, and online (low latency) modes.

  • Develop and evolve the internal ML platform: feature store, model registry, data and model versioning, orchestration, and experimentation environments.

  • Implement model observability: performance monitoring, data drift, concept drift, data quality, alerts, and response plans.

  • Build and maintain CI/CD for ML, including automated testing for data, features, and models, as well as rollout strategies (canary, shadow, blue-green, A/B).

  • Ensure reliability and efficiency of inference services: SLAs, scalability, cost per inference, and resource optimization.

  • Work alongside Data Science to make experiments production-ready, reducing the time between idea and value in production.

  • Ensure governance, traceability, and compliance of models with internal policies, LGPD, and regulatory requirements in the credit sector.

  • Define and disseminate ML engineering best practices: code review, documentation, architecture standards, and technical mentoring of the team.

  • Contribute to architecture decisions and the evolution of the area's technical roadmap.

Qualifications

Essential technical knowledge:

  • Advanced Python, with engineering best practices (testing, typing, packaging, code review).

  • Advanced SQL and experience with distributed processing (Spark, PySpark, or equivalent).

  • Cloud (AWS, GCP, or Azure), with a focus on data and ML services.

  • Containers and orchestration: Docker and Kubernetes.

  • Pipeline orchestration: Airflow, Kubeflow, Dagster, Step Functions, or similar.

  • MLOps tools: MLflow, SageMaker, Vertex AI, Databricks, Feast, or equivalents.

  • Infrastructure as Code (Terraform) and CI/CD practices (GitHub Actions, GitLab CI, Jenkins).

  • Machine learning fundamentals sufficient to engage with Data Science: evaluation metrics, validation, causes of model degradation.

  • APIs and inference services: FastAPI, gRPC, queues, and streaming (Kafka, Kinesis).

  • Monitoring and observability: Prometheus, Grafana, Datadog, Evidently, or similar.

  • Solid experience in software engineering and/or machine learning engineering, with a proven track record of models in production.

  • Experience in high-volume data environments and availability requirements.

Behavioral competencies:

  • Autonomy and ability to lead ambiguous problems to a clear solution.

  • Effective communication with both technical and non-technical audiences.

  • Collaborative mindset and willingness to share knowledge and develop people.

  • Pragmatism: balance between technical rigor and value delivery.

It will be a plus:

  • Experience in the financial, credit, risk, or fraud prevention sector.

  • Experience with regulated models and requirements for explainability and auditing.

  • Experience with LLMs and GenAI in production: RAG, evaluation, guardrails, and inference cost optimization.

  • Contributions to open source projects, technical publications, or speaking engagements.

  • Experience with inference optimization (quantization, ONNX, Triton) or streaming processing.

  • English or Spanish for collaboration with global teams.

Additional Information

Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose driven culture is multi award-winning; World's Best Workplaces™ 2025 (Fortune Global Top 25), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why. Experian is also proud to be an Equal Opportunity and Affirmative Action employer. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.

Experian Careers - Creating a better tomorrow together

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