Data Tech Lead

Job Overview:

• We are seeking a Technical Lead - Data & Analytics  to lead the design and delivery of enterprise-grade capabilities across enterprise data and analytics initiatives. The role requires strong grounding in data concepts, modern data engineering and analytics, metadata and governance, semantic and ontology-driven solutions, and cloud-native delivery practices. The successful candidate will provide technical direction, guide engineering teams, own design quality, and contribute directly to implementation.

Key Responsibilities:

• Technical Leadership: Lead solution design and delivery across Data & Analytics workstreams; translate business requirements into scalable technical designs; conduct design and code reviews; define engineering standards; mentor developers and data engineers; manage technical risks and dependencies.
• Data & Analytics Capabilities: Drive implementation across data ingestion, extraction, profiling, standardization, cataloguing, metadata, lineage, data quality, governance, security, compliance, ontology and entity discovery, knowledge graph, unified query, self-service insights, metrics, monitoring and platform observability.
• Data Architecture & Engineering: Design solutions for structured, semi-structured and unstructured data; define conceptual, logical and physical models; establish ETL/ELT patterns; support lake, Lakehouse and warehouse architecture; ensure performance, scalability, reliability and maintainability.
• Cloud & Platform Engineering: Design and deploy cloud-native solutions on Azure, AWS or GCP; select fit-for-purpose managed services; apply security, networking, identity, resilience, cost and operational best practices.
• DevOps & Containerization: Establish source control, branching, automated build, test and release pipelines; package services and agents using Docker; deploy and operate workloads on Kubernetes; enable configuration, secrets, monitoring, logging and rollback practices.
• Agentic AI Enablement: Contribute to agentic and generative AI solutions, including agent orchestration, tool integration, retrieval-augmented generation, semantic and vector search, knowledge graphs, prompt and response controls, evaluation, guardrails and observability.
• Snowflake Delivery: Design and optimize Snowflake solutions covering ingestion, transformation, modelling, security, governance, data sharing, performance and cost management; integrate Snowflake with cloud, analytics and AI services.
• Stakeholder Collaboration: Partner with architects, product owners, business SMEs, governance teams and delivery leads; communicate designs clearly; sup