Senior Data Consultant

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Building Business Intelligence & Analytics Engineering Teams Across the UK | The Talent Driving The Data and AI Revolution

Senior Data Consultant (Analytics Engineering)

Company Overview

They are a rapidly growing data and analytics platform focused on helping businesses centralise and streamline their data. Their ETL-driven platform integrates machine learning and data science tools to support data-driven decision-making. Serving clients across various industries, including B2B SaaS, retail, and charity, they specialise in building scalable data infrastructures and delivering actionable insights.

With a strong emphasis on financial data, their team is dedicated to enabling clients to make more informed decisions by creating a single source of truth and optimising their data processes. They’re expanding quickly and offer exciting opportunities for individuals passionate about data engineering, analytics, and cloud technologies.

Role Overview

We are seeking a Senior Data Consultant to help companies centralize their data using an ETL-driven decision intelligence platform with integrated machine learning and data science capabilities. This role will be instrumental in developing scalable data infrastructure and enabling clients to make data-driven decisions.

Key Responsibilities

1. Build a single source of truth for clients by structuring and modeling data.
2. Develop scalable data pipelines integrating financial, customer, CRM, ERP, and marketing data.
3. Deliver end-to-end data modelling projects, connecting multiple sources and creating metrics/KPIs.
4. Work primarily with SQL, dbt, and cloud data warehouses (Snowflake, BigQuery, Redshift).

Technical Requirements

1. Strong proficiency in SQL with the ability to write complex queries, optimise performance, and manipulate large datasets efficiently. This includes expertise in database management, data extraction, transformation, and analysis, ensuring seamless data workflows for all stakeholders.
2. Working with modern cloud data warehouses such as Snowflake, BigQuery, or Redshift. You should be comfortable creating robust data models, building scalable pipelines, and ensuring data quality within these cloud environments.
3. Experience with dbt (Data Build Tool), particularly in managing and automating data transformation.
4. Solid understanding of Python, especially in the context of automating data processes, integrating APIs, and implementing machine learning models to enhance data analysis capabilities. Experience with Python libraries like Pandas or NumPy is a plus.
5. Practical experience working with key financial metrics, including revenue, retention, churn, and customer lifetime value, along with a deep understanding of how to extract actionable insights from these data sets to drive business decisions.
6. Demonstrated ability to integrate diverse data sources seamlessly, ensuring smooth data flow across systems, and combining financial, operational, and customer data to create a unified view for accurate reporting and analysis.

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Analyst and Engineering

Industries

IT Services and IT Consulting and Data Infrastructure and Analytics

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