Job 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

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 eSicient

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

• 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.

Other Requirements

Reliable, well-documented data products are available for analytics and AI use

cases.

• Data pipelines are scalable, cost-eSicient, 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 eSectively integrated to support advanced

analytics and GenAI innovation.