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; support estimates, planning, demonstrations, production readiness and issue resolution.

Key Skills:

• 7-10+ years of overall experience, primarily in Data & Analytics, with experience leading technical delivery teams or workstreams.
• Strong understanding of data modelling, data warehousing, Lakehouse architecture, ETL/ELT, data pipelines, data quality, metadata, lineage, governance, master/reference data and analytics consumption patterns.
• Hands-on experience designing and building enterprise-scale data solutions using Python, SQL and relevant data engineering frameworks.
• Experience with at least one major cloud platform: Microsoft Azure, Amazon Web Services (AWS) or Google Cloud Platform (GCP).
• Practical experience with Git-based development, continuous integration and delivery, automated testing, release automation and environment promotion.
• Hands-on exposure to Docker and Kubernetes-based deployment, including configuration, secrets, scaling, monitoring and troubleshooting.
• Working exposure to Agentic AI, Generative AI, LLM-based applications, RAG, vector search, knowledge graphs or multi-agent workflows.
• Experience with Snowflake architecture and development, including data pipelines, security, governance and performance optimization.
• Ability to independently own technical design, guide engineers, review implementation quality and resolve complex delivery issues.
• Strong communication skills, with the ability to explain technical decisions to both engineering and business stakeholders.

Preferred Experience:

• Experience with Databricks, Microsoft Fabric or equivalent modern data and AI platforms.
• Exposure to business glossaries, semantic models, ontologies, entity resolution and enterprise knowledge graphs.
• Experience with orchestration or agent frameworks such as Lang Graph, Lang Chain, Semantic Kernel or similar technologies.
• Understanding of vector databases, model serving, prompt engineering, AI evaluation and responsible AI controls.
• Experience with Infrastructure as Code and platform automation using Terraform, Bicep, CloudFormation or equivalent tools.
• Prior experience delivering metadata-driven, governance-led, self-service or AI-enabled data platforms.