Senior Technical Lead

Project description

This is a strategic data engineering engagement with our client to architect and plan the migration of their entire data processing and ETL estate from Matillion to AWS Glue — a foundational shift in how one of the world's largest financial market infrastructure companies handles its data pipelines.

During this Mobilisation Phase, you'll work jointly with engineering teams to reverse-engineer the existing landscape, design the target-state architecture across every layer (infrastructure, data processing, workflows, dependencies, and operating model), and build the detailed delivery blueprint that will greenlight the full-scale migration.

The work is technically rich and highly collaborative: you'll review and validate job inventories spanning hundreds of ETL workflows, define reusable migration patterns and templates, design a validation and reconciliation framework, run a proof-of-concept to stress-test the approach, and navigate rigorous internal governance — from Architectural Significance Assessments through Architectural Review Boards to a formal Gate 1 decision.

• Target-state architecture design — define and own the target-state AWS architecture across compute, data, and orchestration layers
• HLD/LLD ownership — produce and own High-Level Design and Low-Level Design documentation for migration workloads
• ARB submissions — prepare and present architecture proposals to the Architecture Review Board for approval
• Technical decisions — make and document key technical decisions across the migration program, balancing trade-offs (cost, performance, scalability)
• Pattern definition — define reusable architecture and migration patterns for use across teams/pods
• Agentic AI framework architecture — design the architecture for the Agentic AI migration framework, including its integration with the broader technical landscape

• 5+ years experience
• hands-on experince with AWS Migration
• AWS Glue, Step Functions, Lambda, Event Bridge — deep hands-on experience designing solutions using these core AWS services
• IaC (Terraform) — strong experience defining and managing infrastructure as code with Terraform
• Data platform architecture — proven experience architecting data platforms (data lakes, warehouses, pipelines) at scale
• PySpark — hands-on experience with PySpark for large-scale data processing
• CI/CD — experience designing and implementing CI/CD pipelines for infrastructure and data workflows
• AI/ML frameworks — working knowledge of AI/ML frameworks and their application within architecture design
• Git Hub Copilot — practical experience using Git Hub Copilot within development/architecture workflows

• AWS certification (Data Analytics Specialty or Solutions Architect)
• Experience with BI tools (Quick Sight, Tableau, Power BI)
• Infrastructure as Code experience (Terraform/Cloud Formation)
• Industry experience relevant to your business
• Exposure to streaming data (Kinesis) or ML pipelines (Sage Maker)
• experince in financial domain