Senior Data Engineer

Method is a global design and engineering consultancy founded in 1999. We believe that innovation should be meaningful, beautiful and human. We craft practical, powerful digital experiences that improve lives and transform businesses. Our teams [based in London, New York, Charlotte, Atlanta, Bengaluru, Japan and remote] work with a wide range of organizations in many industries, including Healthcare, Financial Services, Retail, Automotive, Aviation, and Professional Services. Method is part of GlobalLogic, a digital product engineering company. GlobalLogic integrates experience design and complex engineering to help our clients imagine what’s possible and accelerate their transition into tomorrow’s digital businesses. GlobalLogic is a Hitachi Group Company. We are seeking a Senior Data Engineer to join our Data & AI team. You will work within multidisciplinary project teams across a range of client engagements, contributing to the design and development of robust data infrastructure, data pipelines, and scalable data solutions. You think strategically, are comfortable with open-ended data problems, and can contribute meaningfully to client conversations about data architecture, modernization, and AI readiness. The ideal candidate is technically grounded, consulting-minded, and equally comfortable navigating ambiguity, working across disciplines, and communicating clearly with stakeholders at all levels. Travel for client and stakeholder meetings may be required depending on engagement. Key Responsibilities Design, develop, and optimize data pipelines and data infrastructure across a range of client engagements. Contribute to data modeling, master data management, and taxonomy standardization across complex, multi-system environments. Build and test API integrations and prototypes, including working with static data exports and mock stubs in early-phase delivery contexts. Document data governance artefacts - data domains, critical data elements, quality rules, and data lineage - and support clients in building data governance capability. Reverse-engineer legacy systems and extract business logic from poorly documented codebases, complex spreadsheet models, or fragmented data structures. Collaborate with architects, designers, product managers, and client stakeholders to align data solutions with broader digital product and platform goals. Contribute to AI readiness assessments - evaluating data coverage, quality, schema consistency, and lineage to identify gaps and support remediation. Perform code reviews, mentor junior team members, and contribute to best practices across the Data & AI team. Communicate data architecture and pipeline design clearly using schemas, data flow diagrams, and visual artefacts, for both technical and non-technical audiences. Qualifications 5+ years of data engineering experience, including delivery in a consulting or client-facing environment. Proficiency in Python and SQL for data wrangling, transformation, and pipeline development. Experience in data modeling - designing master data schemas and drafting unified taxonomies across fragmented source systems. Master Data Management (MDM) experience: cleansing, mapping, and standardizing inconsistent data into a governed structure. Experience building ETL/ELT pipelines and working with cloud data platforms (AWS, Azure, or GCP). Experience documenting data governance artefacts and contributing to data governance frameworks. Ability to reverse-engineer legacy systems and extract business logic from poorly documented codebases or complex Excel/VBA models. Strong communication skills - able to translate technical decisions clearly for both technical and non-technical stakeholders. Comfortable operating in ambiguity and structuring a path forward without perfect requirements. Willing and able to ramp on new technologies as client engagements evolve. Nice to Have Familiarity with Microsoft Azure services (Azure SQL, Cosmos DB, Azure DevOps). Experience with…