Analytics Engineer (m/f/d)

Statista
Hamburg +1 more
Hybrid

Who this role is best for

Geared toward candidates comfortable with building scalable data models and collaborating with cross-functional teams, this role is ideal for mid-level professionals with a strong technical background in data modeling and cloud data warehouses, based in Germany or willing to work in Hamburg or Berlin.

Best fit for

  • Candidates with 3+ years of analytics or data engineering experience and a focus on cloud data warehouses
    — “3+ years of experience in analytics engineering, data engineering, or a related role”
  • Individuals who thrive on data governance and standardization efforts
    — “Interest in data governance, standards, and scalable data architecture”

Things to consider

  • The role requires a strong command of SQL and data modeling techniques
    — “Strong SQL skills and solid experience in data modeling (e.g., dimensional modeling, star schemas)”
  • Candidates must be able to work with both technical and non-technical stakeholders effectively
    — “Excellent communication skills in English (German is a plus), with both technical and business stakeholders”

How to stand out

  • Highlight your experience with dbt and cloud data warehouses like Snowflake or BigQuery
    — “Design, build, and optimize scalable data models using modern data stack tools (e.g., dbt, Snowflake)”
  • Demonstrate your ability to improve data accessibility and promote best practices
    — “Improve data accessibility and usability across teams by promoting best practices and clear data structures”
  • Showcase your work on data quality checks and monitoring as a core competency
    — “Implement and maintain data quality checks, testing, and monitoring to ensure accuracy and reliability”
  • Emphasize your experience with semantic layer development and governance
    — “Develop and manage semantic layers to standardize and govern key business metrics”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Design, build, and optimize scalable data models using modern data stack tools
  • Transform raw data into clean, reliable, and well-documented datasets for analytics, reporting, and operational use
Typical background
3+ years of experience in analytics engineering, data engineering, or a related role

Skills & requirements

Required

SQLData ModelingdbtSnowflakeData Quality ChecksSemantic LayersData Governance

Preferred

AWSAzurePythonBashGo

Stack & domain

dbtSnowflakeSQLData ModelingCloud Data WarehousesData GovernanceAi-assisted Development WorkflowsCommunicationData EngineeringAnalytics

About the role

Original posting from Statista via Ashby

At Statista, we’re all about facts and data, for we are the world's leading business data platform. By providing reliable and easy-to-use data as well as various data analytics products and services, we empower people worldwide to make fact-based decisions.

Founded in Hamburg in 2007, we have quickly grown into a global company with offices in major cities such as London, New York, Berlin and Tokyo. And we still have a lot of plans. Our constant growth does not only prove our success, but also keeps creating new development and career opportunities for our employees.

We value and celebrate our diverse culture. You are welcome here for who you are, no matter where you come from, what you look like, or whether you prefer bar graphs to pie charts. Your story matters – keep writing it as part of our team.

Are you ready to join us?

Your role

  • Design, build, and optimize scalable data models using modern data stack tools (e.g., dbt, Snowflake)
  • Transform raw data into clean, reliable, and well-documented datasets for analytics, reporting, and operational use
  • Collaborate closely with data engineers, analysts, tracking teams, and business stakeholders to define data requirements and ensure alignment
  • Implement and maintain data quality checks, testing, and monitoring to ensure accuracy and reliability
  • Develop and manage semantic layers to standardize and govern key business metrics
  • Improve data accessibility and usability across teams by promoting best practices and clear data structures
  • Document data models, definitions, and workflows to enhance transparency and data literacy
  • Contribute to data governance, harmonization, and automation initiatives, including enabling AI-driven analytics use cases

Your profile

  • 3+ years of experience in analytics engineering, data engineering, or a related role
  • Strong SQL skills and solid experience in data modeling (e.g., dimensional modeling, star schemas)
  • Hands-on experience with modern data stack tools and technologies (e.g., dbt, CI/CD) and openness to AI-assisted development workflows (e.g., Claude Code or similar)
  • Experience working with cloud data warehouses (e.g., Snowflake, BigQuery, Redshift)
  • Strong attention to detail with a quality-focused and structured working style
  • Interest in data governance, standards, and scalable data architecture
  • Analytical mindset with strong problem-solving skills
  • Excellent communication skills in English (German is a plus), with both technical and business stakeholders, combined with a solid understanding of business requirements and contexts

What we offer

In addition to our great team, culture, and our shared goal of empowering people with data, there are many other things that make Statista a great place to work! Join us and benefit from:

  • Work from abroad up to 30 calendar days a year
  • Hybrid work and flex-time
  • International team and social events
  • Subsidized urban mobility and access to fitness and wellness options
  • Free access to Langdock and all its amazing functionalities
  • Career & training opportunities
  • Attractive locations and modern offices
  • Mental health support with OpenUp

Some of the benefits listed here apply only to the German entity and to Junior-level roles or above.

Source: Statista careers (Ashby)

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