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.