Senior Data Consultant

Harnham
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineering Consultant

Principal Data Consultant

Senior Data Engineer - Databricks

Senior Data Science Consultant, Customer Data & Technology

Senior Data Engineer - Databricks

Senior Data Engineer, Consultant [Urgent]

Senior Data Consultant (Analytics Engineering)


Location:Hybrid (London – Office Days: Tuesday & Thursday)

Company:Fast-growing Data & Analytics Platform

Salary:£65,000 - £85,000


Company Overview

They are a rapidly growingdata and analytics platformfocused on helping businesses centralise and streamline their data. TheirETL-driven platformintegrates machine learning and data science tools to support data-driven decision-making. Serving clients across various industries, includingB2B SaaS,retail, andcharity, 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

  • Build asingle source of truthfor clients by structuring and modeling data.
  • Developscalable data pipelinesintegrating financial, customer, CRM, ERP, and marketing data.
  • Deliver end-to-enddata modellingprojects, connecting multiple sources and creatingmetrics/KPIs.
  • Work primarily withSQL, dbt, and cloud data warehouses(Snowflake, BigQuery, Redshift).
  • UtilizePower BI/Tableaufor reporting.


Technical Requirements

Essential

  • Strong proficiency in SQLwith 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.
  • Working withmodern cloud data warehousessuch asSnowflake,BigQuery, orRedshift. You should be comfortable creating robust data models, building scalable pipelines, and ensuring data quality within these cloud environments.


Desirable

  • Experience with dbt (Data Build Tool), particularly in managing and automating data transformation.
  • Solid understanding ofPython, 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.
  • Practical experience working with keyfinancial 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.
  • Demonstrated ability tointegrate 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.

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.

Data Science Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Data science has become one of the most sought‑after fields in technology, leveraging mathematics, statistics, machine learning, and programming to derive valuable insights from data. Organisations across every sector—finance, healthcare, retail, government—rely on data scientists to build predictive models, understand patterns, and shape strategy with data‑driven decisions. If you’re gearing up for a data science interview, expect a well‑rounded evaluation. Beyond statistics and algorithms, many roles also require data wrangling, visualisation, software engineering, and communication skills. Interviewers want to see if you can slice and dice messy datasets, design experiments, and scale ML models to production. In this guide, we’ll explore 30 real coding & system‑design questions commonly posed in data science interviews. You’ll find challenges ranging from algorithmic coding and statistical puzzle‑solving to the architectural side of building data science platforms in real‑world settings. By practising with these questions, you’ll gain the confidence and clarity needed to stand out among competitive candidates. And if you’re actively seeking data science opportunities in the UK, be sure to visit www.datascience-jobs.co.uk. It’s a comprehensive hub featuring junior, mid‑level, and senior data science vacancies—spanning start‑ups to FTSE 100 companies. Let’s dive into what you need to know.

Negotiating Your Data Science Job Offer: Equity, Bonuses & Perks Explained

Data science has rapidly evolved from a niche specialty to a cornerstone of strategic decision-making in virtually every industry—from finance and healthcare to retail, entertainment, and AI research. As a mid‑senior data scientist, you’re not just running predictive models or generating dashboards; you’re shaping business strategy, product innovation, and customer experiences. This level of influence is why employers are increasingly offering compensation packages that go beyond a baseline salary. Yet, many professionals still tend to focus almost exclusively on base pay when negotiating a new role. This can be a costly oversight. Companies vying for data science talent—especially in the UK, where demand often outstrips supply—routinely offer equity, bonuses, flexible work options, and professional development funds in addition to salary. Recognising these opportunities and effectively negotiating them can have a substantial impact on your total earnings and long-term career satisfaction. This guide explores every facet of negotiating a data science job offer—from understanding equity structures and bonus schemes to weighing crucial perks like remote work and ongoing skill development. By the end, you’ll be well-equipped to secure a holistic package aligned with your market value, your life goals, and the tremendous impact you bring to any organisation.