Apache Spark Technical Lead - Scala, Python

Job Summary

Job Title: Data Engineer
Department: Global Analytics
Reports to: Manager AI Engineering
Level: Senior / Medior (depending on experience)

Role Summary:

The Data Engineer is responsible for designing, building, and operating high-quality,
scalable, and reusable data services that support analytics, AI, and GenAI use cases
across business domains.
In this role, you will design and work hands-on with data pipelines, data models,
orchestration frameworks, storage layers, and observability tooling.
You will collaborate closely with AI Engineers, Data Scientists, Product Owners, and
Platform teams to deliver reliable, well-governed, and self-service data products.

Key Responsibilities

Key Responsibilities:

• Data Platform & Services Engineering
• Build and maintain scalable data pipelines and ingestion frameworks for batch,
  streaming, and event-driven data.
• Develop and maintain modular data models and semantic layers optimized for
  analytics, BI self-service and AI use cases.
• Implement and operate orchestration workflows (e.g., Databricks Workflows)
  and compute engines (Spark, SQL, Python).
• Work with storage technologies such as Delta Lake, ADLS, feature and vector
  stores.
• Data Quality, Governance & Observability
• Implement data quality checks, validations, and monitoring to ensure reliability
  and trust in data products.
• Contribute to data lineage, metadata management, and documentation.
• Apply observability practices using tools such as Great Expectations or Monte
• Carlo.
• Ensure compliance with data governance standards and regulations (e.g., GDPR)
  in collaboration with data governance teams.
• Enablement for AI & Analytics Use Cases
• Deliver curated datasets and reusable data assets for analytics, machine
  learning, and GenAI applications.
• Build pipelines that process structured, graph, and unstructured data (e.g., text,
  documents, images).
• Support AI Engineering teams with data preparation for embeddings, vector
  stores, and retrieval-augmented generation (RAG) pipelines.
• Tooling & Self-Service
• Contribute to data engineering tooling and frameworks that enable e Sicient
  development and deployment of pipelines.
• Develop data pipelines using tools such as dbt and Databricks Lakeflow.
• Support reuse of data services through clear documentation, data contracts,
  templates, and examples.
• Collaboration & Ways of Working
• Collaborate with Data Scientists, AI Engineers, Product Owners, Business SMEs,
  and Platform teams.
• Participate in technical design discussions, code reviews, and architecture
  forums.
• Follow engineering best practices for version control, testing, CI/CD, and
  operational excellence.

Skill Requirements

• Preferred Qualifications
• 5+ years of experience in data engineering and building production-grade data
  pipelines.
• Strong hands-on experience with data platforms such as Databricks.
• Solid knowledge of data modeling, SQL, Spark, and Python.
• Experience with orchestration frameworks, data quality tooling, and
  observability practices.
• Exposure to unstructured data processing and AI/GenAI data pipelines is a
  strong plus.
• Experience working in a global, multi-team environment is beneficial.
• Success in This Role Means
• Reliable, well-documented data products are available for analytics and AI use
  cases.
• Data pipelines are scalable, cost-e Sicient, observable, and easy to operate.
• Data engineers and AI teams can move faster using reusable patterns and selfservice
  data services.
• Structured and unstructured data are e Sectively integrated to support advanced
  analytics and GenAI innovation.

Other Requirements

null