Analytics Engineer

Analytics Engineer

Full-Time 36000 - 60000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Transform raw data into impactful business insights and create reusable analytics assets.
  • Company: Join Spendesk, a forward-thinking company dedicated to data-driven decision making.
  • Benefits: Enjoy flexible work options, health insurance, and great office perks.
  • Why this job: Make a real difference by bridging data engineering and business needs.
  • Qualifications: 3-5 years in analytics engineering with strong SQL and dbt skills.
  • Other info: Be part of a diverse team that values inclusion and personal growth.

The predicted salary is between 36000 - 60000 £ per year.

Transform data into business impact. Spendesk is seeking a skilled Analytics Engineer to join our growing data organization. Reporting to the Head of Data, you will be responsible for transforming raw data into business-ready datasets, building dimensional models, and enabling self-service analytics across the organization.

About the role: As an Analytics Engineer, you will bridge the gap between data engineering and data analysis/business team by creating clean, documented, and reusable analytics assets. You will work closely with Data Engineers, Data Scientists, and business stakeholders to implement data quality testing, build dimensional models, and enable data-driven decision making throughout Spendesk. This role requires an engineer who can translate business requirements into robust data models, implement best practices for data transformation, and create analytics solutions that scale with our business growth.

Our tech environment: Our data platform relies on dbt (core and Cloud), Snowflake, Looker (original), Metabase, and Amplitude for Product analytics. For ingestion/exposure: Airbyte (cloud), Fivetran, Airflow, Hightouch, Segment. We use Github for versioning, CI/CD, and Synq for observability.

Key Responsibilities:

  • Transform raw data into business-ready datasets using dbt and modern data stack tools.
  • Build and maintain dimensional models that serve BI and Product needs.
  • Implement business logic and calculations in the data transformation layer.
  • Create reusable analytics assets that can be leveraged across multiple use cases.
  • Ensure data models follow best practices for performance, maintainability, and scalability.
  • Implement comprehensive data quality testing using dbt tests.
  • Develop and maintain data quality monitoring and alerting systems.
  • Create data validation rules that catch issues before they impact business decisions.
  • Establish data quality metrics and SLAs for analytics datasets.
  • Collaborate with all stakeholders to resolve data quality issues at the source.
  • Enable self-service analytics by creating intuitive, well-documented data models.
  • Partner with business and product stakeholders to understand analytics requirements and translate them into technical solutions.
  • Build metric definitions and calculations that ensure consistency across the organization.
  • Create data documentation and maintain data catalogs for business and product stakeholders.
  • Provide training and support to stakeholders on analytics tools and data interpretation.

Stakeholder collaboration:

  • Work closely with analysts and data scientists to provide analysis-ready datasets.
  • Collaborate with business stakeholders to understand requirements and design appropriate data solutions.
  • Partner with Data Engineers to ensure optimal data pipeline design and performance.
  • Communicate technical concepts clearly to both technical and business audiences.

What we’re looking for:

Experience & Background:

  • 3-5 years of experience in analytics engineering, data analytics, or related data roles.
  • Proven track record of building data models and transformations in production environments.
  • Experience working with business stakeholders to translate requirements into technical solutions.
  • Background in implementing data quality testing and monitoring practices.

Technical Requirements:

  • Expert proficiency in SQL and advanced capabilities.
  • Hands-on experience with dbt for data transformation and modeling.
  • Experience with cloud data warehouses (Snowflake).
  • Experience with data quality testing frameworks (e.g. dbt tests).
  • Proficiency in version control systems (Git, GitHub).
  • Understanding of dimensional modeling concepts and best practices.

Analytics & Business Skills:

  • Strong understanding of business intelligence and analytics concepts.
  • Experience with data visualization tools and self-service analytics platforms.
  • Ability to translate business requirements into technical data solutions.
  • Knowledge of statistical concepts and data analysis methodology.

Diversity & Inclusion: At Spendesk, we’re committed to fostering an environment where all differences are encouraged, supported and celebrated. We’re building our culture for everyone, with everyone. Our goal is to attract and build a diverse, equal and inclusive team, where everyone feels welcome and we truly embrace and encourage people from all backgrounds to apply.

Benefits:

  • Flexible on-site and remote policy.
  • Lunch 60% funded by Spendesk (Swile Card).
  • Alan Premium health insurance.
  • A Gymlib pass to let off steam after a productive day at work.
  • Access to Moka.care for emotional and mental health wellbeing.
  • Great office snacks to fuel your day.
  • A positive team to work with daily!

Analytics Engineer employer: Spendesk

Spendesk is an exceptional employer that prioritises employee growth and well-being, offering a flexible work environment that balances on-site and remote opportunities. With a strong commitment to diversity and inclusion, Spendesk fosters a collaborative culture where innovative ideas thrive, supported by comprehensive benefits such as premium health insurance and wellness resources. As an Analytics Engineer, you will not only contribute to impactful data solutions but also enjoy a supportive team atmosphere that encourages professional development and personal fulfilment.
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Contact Detail:

Spendesk Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Analytics Engineer

✨Tip Number 1

Network like a pro! Reach out to current employees at Spendesk on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Analytics Engineer role.

✨Tip Number 2

Show off your skills! Prepare a portfolio showcasing your previous work with data models and transformations. This will give you an edge during interviews and demonstrate your hands-on experience.

✨Tip Number 3

Practice makes perfect! Brush up on your SQL and dbt skills before the interview. You might be asked to solve real-world problems, so being sharp on these tools is crucial.

✨Tip Number 4

Apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in joining the Spendesk team.

We think you need these skills to ace Analytics Engineer

SQL
dbt
Data Transformation
Dimensional Modeling
Data Quality Testing
Cloud Data Warehouses (Snowflake)
Version Control Systems (Git, GitHub)
Data Visualization Tools
Business Intelligence Concepts
Data Analysis Methodology
Collaboration with Stakeholders
Technical Communication
Analytics Solutions Development
Self-Service Analytics Enablement

Some tips for your application 🫡

Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Analytics Engineer role. Highlight your experience with SQL, dbt, and any relevant data transformation projects to catch our eye!

Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about analytics engineering. Share specific examples of how you've transformed data into actionable insights and how you can contribute to our team at Spendesk.

Showcase Your Technical Skills: Don’t shy away from detailing your technical expertise! Mention your hands-on experience with cloud data warehouses like Snowflake and your familiarity with data quality testing frameworks. We love seeing those skills in action!

Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!

How to prepare for a job interview at Spendesk

✨Know Your Tools Inside Out

Make sure you’re familiar with the tech stack mentioned in the job description, especially dbt and Snowflake. Brush up on your SQL skills and be ready to discuss how you've used these tools in past projects.

✨Showcase Your Data Transformation Skills

Prepare examples of how you've transformed raw data into business-ready datasets. Be ready to explain your thought process and the impact your work had on decision-making within your previous roles.

✨Understand Business Needs

Spend some time researching Spendesk and its business model. Think about how analytics can drive their success and be prepared to discuss how you would translate business requirements into technical solutions.

✨Communicate Clearly

Practice explaining complex technical concepts in simple terms. You’ll need to collaborate with both technical and non-technical stakeholders, so being able to bridge that gap is crucial.

Analytics Engineer
Spendesk
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