Senior Analytics Engineer (m/f/d)

AutoScout24
Berlin +1 more
On-site

Who this role is best for

Candidates with advanced data modeling expertise and a history of leading technical initiatives will find this senior role in Berlin or Munich ideal.

Best fit for

  • Candidates with 7+ years of experience in analytics engineering and a track record of leading complex technical projects
    — “7+ years of experience in Analytics Engineering, Data Engineering, or a related data role”
  • Individuals with strong cloud-native analytics platform experience, particularly with AWS services
    — “Strong experience with cloud-native analytical platforms, ideally AWS services such as Glue, Athena, S3, or MWAA”
  • Candidates who can bridge technical and business stakeholders through clear communication and reasoning
    — “The ability to influence technical and business stakeholders through clear reasoning and communication”

Things to consider

  • This role demands a high level of technical ownership and decision-making across multiple teams
    — “Own complex analytics engineering workstreams from problem framing through delivery and adoption”
  • Candidates must be capable of mentoring and guiding junior engineers and analysts
    — “Mentor analytics engineers and analysts through reviews, pairing, and technical guidance”

How to stand out

  • Highlight your experience with semantic modeling and AI-driven analytics in your resume and interview responses
    — “Experience with semantic layers, shared metric frameworks, or AI-driven analytics is a strong advantage”
  • Emphasize your ability to improve modeling standards and automate quality checks
    — “Turn recurring quality or delivery issues into reusable standards, automation, and documentation”
  • Showcase your history of resolving ambiguity and aligning stakeholders on technical decisions
    — “Experience setting technical direction, resolving ambiguity, and aligning stakeholders”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

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

What success looks like

  • Ownership of complex technical workstreams
  • Setting technical direction for analytical systems
  • Improving standards and practices
  • Driving consistency in KPIs and metrics
Typical background
7+ years of experience in Analytics Engineering or Data EngineeringExperience with cloud-native analytical platforms like AWS

Skills & requirements

Required

Analytics EngineeringData EngineeringAdvanced SQLCloud-native PlatformsPython For Data ProcessingData Quality And TestingData Privacy And SecurityData GovernanceTechnical Leadership

Preferred

Semantic LayersShared Metric FrameworksAi-driven Analytics

Stack & domain

SQLPythonAWSGlueAthenaS3MWAAData EngineeringData ModelingCloud-native Analytical PlatformsCommunicationProblem SolvingDecision MakingMentoringCollaborationLeadershipHealthcareData PrivacyData SecurityData GovernanceCompliance

About the role

Original posting from AutoScout24 via Greenhouse

A career driven by you. Join AutoScout24 Group and experience life in the fast lane. We’re not just leading the market; we’re creating it. We’re constantly innovating, evolving and transforming things for our customers. We’re driven, ambitious and determined to do our best work, together. For a career that doesn’t stand still, where you can put your ideas in the driving seat and change the way the world moves, join our international community. 

As a Senior Analytics Engineer, you will be a senior technical contributor within the Analytics Engineering team and help shape the data foundations behind reporting, self-service analytics, and AI-driven decision-making. You will own complex technical workstreams, set direction for team-level analytical systems, and help improve the standards, practices, and architecture used across the team. 

What youʼll do 

Own complex analytics engineering workstreams from problem framing through delivery and adoption 

Design and evolve scalable analytical models, semantic layers, and reusable data products 

Set technical direction for team-owned models and analytical systems 

Resolve ambiguity and align stakeholders around clear, pragmatic technical decisions 

Improve modeling standards, documentation, testing, review practices, and quality gates 

Drive consistency in KPIs, metrics, and analytical logic 

Partner with Data Platform, Product Analytics, Engineering, and Data Governance teams 

Improve the performance, reliability, and cost efficiency of AWS-based analytical workloads 

Contribute to AI-driven capabilities such as text-to-SQL, semantic modeling for AI, automated insights, and conversational analytics 

Guide complex refactoring, migrations, and technical debt reduction 

Review analytical solutions for correctness, maintainability, privacy, security, performance, and business fit 

Mentor analytics engineers and analysts through reviews, pairing, and technical guidance 

Turn recurring quality or delivery issues into reusable standards, automation, and documentation 

Connect technical decisions to product, operational, and business outcomes 

You come with 

7+ years of experience in Analytics Engineering, Data Engineering, or a related data role 

Deep experience designing, building, and scaling analytical models and reusable data products 

Advanced SQL skills and strong knowledge of dimensional and semantic modeling 

Strong experience with cloud-native analytical platforms, ideally AWS services such as Glue, Athena, S3, or MWAA 

Practical Python experience for data processing, automation, validation, or troubleshooting 

A strong track record of owning complex technical initiatives within a team or across closely related teams 

Experience setting technical direction, resolving ambiguity, and aligning stakeholders 

Strong understanding of data quality, testing, observability, documentation, and operational ownership 

Experience improving modeling standards, development workflows, review practices, or quality gates 

The ability to influence technical and business stakeholders through clear reasoning and communication 

Experience mentoring analytics engineers, analysts, or other data practitioners 

A good understanding of data privacy, security, governance, and compliance in analytical systems 

Experience with semantic layers, shared metric frameworks, or AI-driven analytics is a strong advantage 

A proactive, collaborative, and solution-driven mindset 

What success looks like 

You act as a trusted senior technical contributor within the team 

You own complex workstreams and help the team navigate difficult technical decisions 

Core models and semantic assets become more scalable, reliable, and reusable 

Engineering quality improves through stronger standards, reviews, mentoring, and automation 

Stakeholders trust your technical judgment and involve you early in important decisions 

You improve not only individual data products, but also the systems and practices around them 

A culture of growth  

With over 50 nationalities represented, we’re proud to welcome people of all backgrounds to our growing community, and we’re always finding new ways to help people feel welcomed and included. Here, you’ll have the tools, training and support you need to work in a way that suits you, learn new skills and reach your potential.  

For a career where you can drive our business and shape your future, apply now. 

Source: AutoScout24 careers (Greenhouse)

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