Role / Job Title: Data Engineer Work Location: Norwich-3 Days (Flexible) Duration of Assignment: 6 Months The Role Hands on experience to Design & Develop a ETL pipelines in aws Glue for ingesting data from sql server or other sources Hands on experience on PySpark & Python Experience of implementing change data capture pipelines using aws dms for near-real time or batch ingestion Experience of developing incremental loads Glue job pipelines Experience of building or using reusable glue templates Strong SQL skills (joins, subqueries, window functions) Knowledge in dbt coding for data pipelines/Data warehousing Work with business architects and business teams to define data models and ingestion strategies Participate in sprint planning, code reviews, and deployment pipelines (CI/CD) Your Responsibilities Solution & Data Model Design Hands on experience to Design & Develop a ETL pipelines in aws Glue for ingesting data from sql server or other sources Hands on experience on PySpark & Python Experience of implementing change data capture pipelines using aws dms for near-real time or batch ingestion Experience of developing incremental loads Glue job pipelines Experience of building or using reusable glue templates Strong SQL skills (joins, subqueries, window functions) Knowledge in dbt coding for data pipelines/Data warehousing Work with business architects and business teams to define data models and ingestion strategies Participate in sprint planning, code reviews, and deployment pipelines (CI/CD) Your Profile Essential Skills / Knowledge / Experience 3 4 years of hands-on experience designing and implementing Reltio solutions for Customer MDM Strong understanding of Customer 360 concepts: party, identities, hierarchies/householding, interaction history, deduplication, golden record, and consent/privacy considerations Practical experience with: Reltio Data Model (entities, attributes, relations, reference data) Match & Merge configuration, survivorship rules, crosswalks Reltio APIs/Connectors, event-driven integrations, and data onboarding Data Quality setup, cleansing, validation, and stewardship workflows Ability to engage with senior client architects/SMEs, communicate design patterns, and defend decisions with clear rationale and impact analysis Proficiency in data modeling (conceptual/logical/physical), metadata, and governance patterns Familiarity with JSON-based configuration, API specs, and common integration styles (REST, batch files, streaming) Design Thinking & Pattern Literacy: Can select and tailor patterns (e.g., registry vs. consolidation, survivorship strategies, incremental vs. full sync) to business context Analytical & Data-Driven: Uses profiling metrics and DQ findings to refine match rules and thresholds Clear Communication: Distills complex MDM concepts for senior stakeholders; creates crisp design artifacts Quality & Governance Mindset: Builds for auditability, lineage, and stewardship Collaboration: Works closely with architects, engineers, and business SMEs; mentors junior team members Preferred Qualifications AWS, s3, DMS, Athena, Redshift, DBT, Glue and other AWS services Desirable Skills / Knowledge / Experience Pyspark, dimensional & relational modelling, git, GitHub, code pipeline, Acturis (business domain knowledge)