Senior Technical Lead

Role Summary

Data Platform and Services Engineering

• Build and maintain scalable data pipelines and ingestion frameworks for batch, streaming, and event-driven data.
• Develop modular data models and semantic layers optimized for analytics, BI self-service, and AI use cases.
• Implement and operate orchestration workflows and compute engines such as Databricks Workflows, Spark, SQL, and Python.
• Work with Delta Lake, ADLS, feature stores, and vector stores.

Data Quality, Governance, and 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.
• Support compliance with data governance standards and regulations such as GDPR.

AI and Analytics Enablement

• Deliver curated datasets and reusable data assets for analytics, machine learning, and GenAI applications.
• Build pipelines for structured, graph, and unstructured data including text, documents, and images.
• Prepare data for embeddings, vector stores, and retrieval-augmented generation pipelines.

Tooling and Self-Service

• Contribute to data engineering tooling and frameworks that enable efficient pipeline development and deployment.
• Develop data pipelines using tools such as dbt and Databricks Lakeflow.
• Support reusable data services through documentation, data contracts, templates, and examples.

Collaboration and 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.

Preferred Qualifications

• At least 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 or GenAI data pipelines is a strong plus.
• Experience working in a global, multi-team environment is beneficial.

Success in This Role