Senior Data Engineer

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.

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

Requirements

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 API mock development or prototyping in early-phase delivery contexts; Experience with ERP or supply chain data structures; Experience modernizing legacy integrations (SOAP/WCF to REST); Database consolidation or migration experience (SQLite, MSSQL, or cloud databases); Familiarity with modern orchestration and transformation tools (Airflow, dbt, Dagster, Prefect); Background in manufacturing, engineering, or cost modelling domains; Familiarity with enterprise integration platforms such as MuleSoft; Experience with machine learning or predictive analytics frameworks

Benefits

Continuing education opportunities; Flexible PTO and work-from-home policies; Pension; Health and dental benefits; Company lunches, company outings, along with a lot of snacks; Health and wellness programs; Other location specific perks (just ask!)