Senior Data Engineer

The Senior Data Engineer leads globally and collaborates with regional data engineering teams to design, deliver, and continuously improve high-quality, scalable, reusable enterprise data products and services. These services support analytics, AI, and GenAI initiatives across global business domains.

The role owns the design and engineering of data models, orchestration pipelines, storage strategies, compute environments, and observability stacks, with a focus on self-service interfaces for AI engineers, analysts, and data scientists. It combines deep technical expertise and strategic thinking, including support for feature engineering, unstructured data, and GenAI use cases.

Data Platform & Services Engineering

• Contribute to the design and implementation of scalable data pipelines, ingestion frameworks, and processing engines for batch, streaming, and event-driven data.
• Architect and maintain modular data models and semantic layers optimized for analytics, AI, and self-service exploration.
• Define and manage orchestration frameworks such as Databricks Workflows; compute engines including Spark, SQL, and Python; and storage strategies including Delta Lake, ADLS, and Online Feature Stores.

Data Quality, Governance & Observability

• Establish robust data quality monitoring, lineage tracking, metadata management, and anomaly detection processes.
• Collaborate with data management and governance teams to ensure compliance with global data policies, including GDPR and internal data quality standards.
• Implement observability standards using tools and platforms such as Great Expectations and Monte Carlo.

Enablement for AI Products

• Deliver curated datasets and domain-specific knowledge layers for traditional AI products and agentic AI applications.
• Design pipelines that process and enrich structured, graph, and unstructured data, including text, documents, and images, for ML models and LLM or RAG-based systems.
• Partner with AI Engineering teams to support vector stores, embedding generation, and context retrieval layers.

Tooling & Self-Service Interfaces

• Define and implement tooling frameworks and APIs for data and AI product development, monitoring, and access control.
• Co-design and manage a developer platform for developing and deploying data pipelines using tools and frameworks such as dbt and Databricks Lakeflow.
• Promote reuse of data services across domains through documentation, data lineage, templates, data contracts, and support.

Leadership & Collaboration

• Manage and mentor data engineering squads, lead technical design reviews, and provide coaching.
• Collaborate with Data Scientists, ML and AI Engineers, Product Owners, Business SMEs, and Platform teams.
• Contribute to the global data engineering vision, architecture principles, and capability roadmap.

Qualifications

• 7–10 years of experience in data engineering, platform development, and/or large-scale data systems.
• Proven leadership of engineering teams.
• Strong hands-on knowledge of modern data platforms such as Databricks.
• Experience with data pipeline orchestration, data modeling, data quality frameworks, and observability stacks.
• Familiarity with unstructured data processing and GenAI enablement pipelines is highly desirable.
• Comfortable working in a matrixed global organization with both global and regional teams.