Analytical Engineer

Harnham; Greater London; Hybrid; GBP 80,000 - 110,000; Full time

Job summary

Harnham in London is seeking a Contract Analytical / Data Engineer to join a high-impact data programme on a hybrid basis. You will work within a Snowflake and dbt environment to transform, analyse and operationalise customer and prospect data across the business.

The role requires quick understanding of complex models and delivering meaningful changes from day one. You will collaborate with engineering, analytics and Data Science stakeholders, operating autonomously in an evolving project

Qualifications

• Strong data engineering experience in modern cloud data environments.
• Excellent SQL skills and hands-on dbt expertise.
• Deep experience with Snowflake.
• Ability to understand complex data models, schemas and pipelines.
• Experience tracing data lineage and assessing downstream impact of changes.
• Strong analytical skills to validate and troubleshoot data outputs.
• Comfortable with evolving requirements and autonomous work.

Responsibilities

• Investigate existing dbt models, data pipelines and Snowflake schemas to understand data flows and dependencies.
• Analyse data lineage across source systems, transformations and downstream data exposures.
• Build new data exposures and enhance existing models to support evolving business requirements.
• Modify transformation logic and validate the impact of changes through data analysis and testing.
• Support scenario modelling, data quality initiatives, QA activities and operational readiness.
• Collaborate with engineering, analytics and Data Science stakeholders while working autonomously in an evolving project environment.

Skills

SQL

dbt

Snowflake

Data Modeling

Data Lineage

Python

Data Quality

Independent Work

Stakeholder Mgmt

AI workflows

Job description

Contract Analytical / Data Engineer

Location: London Hybrid

This is an opportunity to join a high-impact data programme at a pivotal stage, moving from strategy into execution. You'll work within an established Snowflake and dbt environment, helping shape how customer and prospect data is transformed, analysed and operationalised across the business. The project requires someone who can quickly understand complex existing data models and deliver meaningful change from day one.

The Company

They are a technology-led organisation with a mature data ecosystem and a strong culture of data-driven decision making. Their teams are focused on improving how customer and prospect data supports commercial and operational outcomes. With a large-scale Snowflake environment and extensive dbt implementation already in place, they are investing heavily in the next phase of their data strategy. The team works closely with Data Science and business stakeholders to deliver practical, high-value outcomes.

The Role and Deliverables

• Investigate existing dbt models, data pipelines and Snowflake schemas to understand data flows and dependencies.
• Analyse data lineage across source systems, transformations and downstream data exposures.
• Build new data exposures and enhance existing models to support evolving business requirements.
• Modify transformation logic and validate the impact of changes through data analysis and testing.
• Support scenario modelling, data quality initiatives, QA activities and operational readiness.
• Collaborate with engineering, analytics and Data Science stakeholders while working autonomously in an evolving project environment.

Your Skills & Experience

• Strong Data Engineering experience within modern cloud data environments.
• Excellent SQL skills and deep hands-on expertise with dbt.
• Strong experience working with Snowflake.
• Demonstrated ability to understand and navigate complex existing data models, schemas and transformation pipelines.
• Experience tracing data lineage and assessing the downstream impact of engineering changes.
• Strong analytical skills, with the ability to validate and troubleshoot data outputs.
• Comfortable working in environments where requirements evolve over time.
• Proven ability to operate independently and quickly become productive in established data estates.
• Experience with Python is beneficial.
• Experience partnering with Data Science teams or working with customer and operational data is advantageous.
• Familiarity with AI-assisted engineering workflows is beneficial.