Data Engineer with Modelling Experience

Role Title Data Engineer with Modelling Experience (DE)

Location Greater London

Duration 6 months

How many days in a week Work from Office. 3 Days Week / Office

Data Engineer with Modelling Experience (DE)

Role Overview

We are seeking a skilled Data Engineer with strong modelling experience across data warehouse and graph paradigms. The ideal candidate is proficient across the GCP data stack, CI/CD pipelines, infrastructure-as-code, and data governance tooling, and can operate independently in a complex cloud-native environment.

Key Responsibilities

• Design, build, and maintain scalable data pipelines and transformation workflows

• Implement and manage CI/CD pipelines within GitLab

• Deploy and maintain Terraform modules for repeatable infrastructure provisioning

• Develop and orchestrate Airflow DAGs in Google Cloud Composer

• Model data at warehouse and graph levels to support platform requirements

• Manage SpannerDB schema design and querying

• Apply BigQuery knowledge catalog and data governance practices

• Collaborate with stakeholders and contribute to large-scale project delivery

Required Skills & Experience

SQL & BigQuery

• Advanced SQL (window functions, arrays and structs, DDL, DML, UDFs, CTEs)

• Dataform for SQL modelling and transformation tasks

• BigQuery computational model — partitioning, clustering, query optimisation, pricing model (on-demand vs slots)

• Knowledge Catalog integrations: CDE identification, metadata, policy tagging, data quality scans (Data Contracts)

• Understanding of BigQuery IAM access principles

• Experience using GraphQL

Python

• Proficient Python development for data engineering tasks

Data Modelling

• Data warehouse and graph modelling

• Normalisation and denormalisation

• Conceptual, logical, and physical modelling

• SCD and time series modelling

• Medallion architecture concept

• Translation of business requirements into modelling outputs

Airflow / Composer

• DAG creation and execution

• Utilising Airflow in a cloud environment

• Integration with GCS, BigQuery, and Dataform

GCS (Google Cloud Storage)

• Bucket and blob structure

• Storage classes and retention policies

• Bucket access management via IAM

GitLab

• Branch management, commits, merges, repository maintenance

• CI/CD pipeline setup and execution in GitLab

Terraform (IaC)

• Terraform fundamentals and GitLab integration

• Deploying and modifying repeatable modules

SpannerDB

• Querying Spanner databases

• Relational modelling and schema design (primary keys, interleaved/global indexes)

• Use of interleaved tables, strong vs stale reads

Pub/Sub

• Understanding of event-driven messaging with Pub/Sub

GIS Data

• Knowledge of GIS data and engines (coordinate conversions, common file formats, BigQuery GIS)

Domain Knowledge

• Telecommunications: network topology, KPIs, telemetry, asset lifecycle

Stakeholder Management

• Proven experience in stakeholder engagement on large-scale projects

Nice to Have

• Dataflow (Apache Beam)