Data Analyst

DSV-Gruppe

Data Analyst managing data-driven projects for Sparkassen-Finanzgruppe in areas like Power BI and Machine Learning. Collaborating to create reports and analytics for business objectives.

Posted 7/18/2026full-timeBerlin • 🇩🇪 GermanyMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data analytics, including proficiency in Power BI, Microsoft Fabric, Python, and SQL, while effectively managing data-driven projects and collaborating with stakeholders to deliver actionable insights. Capable of translating complex requirements into viable analytical solutions and optimizing processes in an agile environment.

Highest-signal resume keywords

Power BI AdministrationMicrosoft Fabric DevelopmentPython ProgrammingSQL AnalysisData Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisMachine LearningDAXETL/ELT ApproachesData GovernanceData StructuresQuantitative AnalysisStatistical AnalysisCloud PlatformsBusiness Informatics

Soft Skills

Strong CommunicationStructured Working StyleIndependent WorkConsulting SkillsCollaboration

Tools & Technologies

Power BIMicrosoft FabricAzureData WarehousingKPI Frameworks

Industry Keywords

Data AnalyticsBusiness StakeholdersAgile EnvironmentProject ManagementRequirements Gathering

Tech Stack

Tools & technologies

AzureCloudETLPythonSQL

About the role

Key responsibilities & impact

Independently manage data-driven projects end-to-end — from requirements gathering through analysis of large, heterogeneous datasets to deriving well-founded recommendations for action
Technically maintain and further develop our Power BI and Microsoft Fabric landscape, with a focus on workspaces, permissions, capacities, data models, and governance
Perform complex analyses using Python and SQL and contribute to the development and production deployment of machine learning models
Collaborate with clients to develop meaningful reports, dashboards, and KPI frameworks to make business objectives measurable and actionable
Advise business areas, structure and refine requirements, and translate them into viable analytical solutions
Communicate results appropriately to stakeholders and management, and moderate workshops or alignment meetings from a subject-matter perspective

Requirements

What you’ll need

Degree in a quantitative or technical discipline, e.g., Data Science, Statistics, Mathematics, or (Business) Informatics/Computer Science
Several years of relevant professional experience in the data analytics domain
Experience at the interface between end clients/business stakeholders and technical implementation teams
Ability to structure and translate complex requirements
Willingness to take on consulting and product-management tasks
Proficient in Power BI and Microsoft Fabric, including technical administration, data modeling, DAX, as well as governance and capacity topics
Experienced in Python and SQL, able to analyze and evolve even complex data structures
Familiar with modern cloud, data warehouse, and ETL/ELT approaches, ideally on Azure or similar platforms
Strong communication skills, structured working style, and ability to work independently
Motivated to drive topics forward in an agile environment and take ownership for your areas
Continuously looking for optimizations of team structures and processes and enjoy collaborating in small project/product teams

Benefits

Comp & perks

High-impact work — our products are available to over 50 million Sparkasse customers
A modern workplace at one of our locations
Attractive options for mobile/remote work
Flexible working hours in full-time (39 hours) or part-time
Competitive compensation, employer contributions to capital-forming benefits (VWL) and retirement plans, and various employee discounts
Subsidy for a job ticket (commuter pass)
30 days vacation plus 2 company holidays (Dec 24 and Dec 31)
A variety of training and development opportunities
An inspiring work atmosphere, a casual dress code, and friendly colleagues
A trusting, warm team environment with flat hierarchies