We are looking for a Senior AWS Data Architect who can design enterprise-grade data platforms on AWS and bring strong AI engineering judgement to modern data and AI architectures.

You will be responsible for defining how our clients' data platforms are structured, secured, governed, and made useful, including the data and retrieval foundations required for modern LLM and agentic applications.

This is a senior individual-contributor role with architecture ownership and client-facing responsibility. You will lead technical design, solve complex data and AI architecture problems, and remain close enough to the implementation to validate that your designs work in practice.

Roles and Responsibilities :

- Own end-to-end data architecture on AWS, including lakehouse and warehouse design, ingestion patterns, storage, compute selection, and cost modelling.

- Design and review solutions using S3, Glue, Lake Formation, EMR, Athena, Redshift, Kinesis, MSK, Lambda, Step Functions, DynamoDB, RDS/Aurora, and related AWS services.

- Design data foundations for AI and agentic applications, including embedding pipelines, vector storage, retrieval strategies, context assembly, guardrails, and evaluation datasets.

- Define architecture for Generative AI workloads on AWS, including Amazon Bedrock, SageMaker, and integration patterns for third-party model providers.

- Define and implement data governance, security, and compliance architectures, including IAM, least-privilege access, encryption, PII handling, data classification, lineage, cataloguing, and auditing.

- Establish reference architectures, data contracts, naming conventions, and data modelling standards.

- Review engineering designs and ensure solutions align with established architecture and engineering standards.

- Design and manage cloud cost optimisation through right-sizing, storage tiering, workload placement, and explicit architecture trade-offs.

- Lead technical discussions with enterprise clients, including discovery, architecture presentations, technical decision-making, and solutioning.

- Mentor data and ML engineers and contribute to strengthening architecture capabilities across delivery teams.

- Stay close to implementation through prototyping, technical validation, code reviews, and architecture reviews.

Required Skills :

- 8+ years of experience in data engineering, data platforms, or data architecture, with at least 4 years of hands-on AWS architecture experience.

- Proven experience owning and delivering at least two large-scale production data platforms, with the ability to explain architectural decisions, trade-offs, and lessons learned.

- Deep hands-on expertise with AWS data services, including S3, Glue, Lake Formation, EMR/Spark, Athena, Redshift, Kinesis, MSK, Lambda, and Step Functions.

- Strong data modelling expertise across dimensional modelling, Data Vault, and lakehouse architectures.

- Expert-level SQL skills.

- Production experience with Apache Spark and at least one lakehouse table format such as Apache Iceberg, Delta Lake, or Apache Hudi.

- Strong understanding of AI engineering and modern LLM application patterns, including RAG, embeddings, vector search, prompt and context design, and evaluation of non-deterministic AI systems.

- Strong experience with Infrastructure as Code, using Terraform or AWS CDK, and CI/CD for data platforms.

- Strong Python skills, with the ability to prototype solutions and perform meaningful code reviews.

- Strong security and governance expertise, including IAM, KMS, VPC architecture, and experience working within compliance frameworks such as SOC 2, ISO 27001, HIPAA, GDPR, or DPDP.

- Excellent written and verbal communication skills, with the ability to communicate architecture and technical trade-offs to enterprise stakeholders and client teams.

- Experience with Amazon Bedrock, SageMaker, or agentic AI frameworks such as LangGraph, LlamaIndex, Strands, CrewAI, or similar is an advantage.

- Experience with Snowflake or Databricks alongside native AWS data services is an advantage.

- Experience with streaming and real-time data architectures, including Flink, Spark Structured Streaming, Debezium, or AWS DMS is an advantage.

- Experience with Kubernetes/EKS and containerised data workloads is an advantage.

- Experience in consulting or services environments with multiple clients is an advantage.

- Experience with cloud migrations or multi-cloud environments, particularly on-premises, GCP, or Azure to AWS, is an advantage.

- Experience in pre-sales, RFPs, solution architecture, or client solutioning is an advantage.