Top Data Analyst Jobs in Czechia

As a Data Analyst, you will work on transformation projects with large banks, focusing on data analysis, business process optimization, and agile method implementation.

As a Data Analyst, you will analyze data, optimize business processes, and support transformation projects for major banks. Collaboration in an agile team is key.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

The Data Analyst will work with Business Teams to gather needs, analyze business performance, create reports and dashboards using Power BI, and conduct statistical analyses with R or Python.

Own end-to-end product analytics for the Pro team: build production-grade pipelines and dbt models, define north-star metrics, design A/B tests and causal inference frameworks, create dashboards and metric frameworks, embed AI tooling, and translate insights into product recommendations for leadership.

Work as a business/data analyst on banking transformation projects: analyze and validate large datasets, define data quality rules, perform as-is process and data analysis, write requirements and data mappings, support implementation, and progressively lead sub-projects and small teams.

Join an agile team delivering data management and transformation projects for leading banks. Analyze large datasets, define requirements, perform data mapping and data quality management, modernize processes, and support implementation. Gradually take ownership of topics and lead smaller sub-projects.

Analyze large datasets to identify trends and deliver actionable insights; design and monitor KPIs; build and maintain dashboards and reporting frameworks; conduct ad hoc analyses; ensure data quality and support reporting infrastructure in partnership with product and business teams.

Early-career Data Analyst responsible for designing and owning core metrics and dashboards, analyzing transaction/customer/credit data to inform credit, fraud, AML, portfolio and growth decisions, building data pipelines and automations with SQL and Python, partnering with cross-functional teams, and owning compliance reporting. Emphasis on turning ambiguous problems into reliable analytical systems and applying GenAI/LLMs for practical automation.

Analyze and instrument offsite discovery and SEO/GEO performance for ecommerce sites. Build integrations, ETL pipelines, and models to track organic and AI-driven traffic. Create tooling and frameworks for technical site audits, landing page modelling, SEO experiments, and enablement for product and customer teams.

Analyze and improve multilingual text data quality by developing parsers, evaluating classifiers, translating taxonomies (Skills, Titles, Occupations), cleaning data in Excel, and applying language and text-analysis expertise in collaboration with Linguistics and Engineering teams.

Support multilingual data quality by analyzing and cleaning text data, developing parsers, evaluating text classifiers, translating taxonomies (Skills, Titles, Occupations), and partnering with Linguistics and Engineering to deliver high-quality global data.

Embedded within the commercial team, the Senior Data Analyst will own core sales and revenue metrics, improve pipeline and forecast accuracy, investigate CRM data quality, size commercial opportunities, and analyze partner, retention, and renewal performance. The role translates ambiguous business questions into actionable recommendations, defines trusted metric frameworks, communicates insights to senior stakeholders, and increases self-service analytics adoption across non-technical commercial teams.

Ensure data accuracy and reliability by writing Python scripts, maintaining Airflow pipelines, investigating anomalies, processing large datasets, building internal data-quality tools, and collaborating with engineering and client teams to deliver clean, production-ready data.

Partner with Marketing, Sales, Finance, Product and other teams to turn business questions into actionable analysis. Own end-to-end analytics: build datasets and dashboards (dbt, LookML), maintain metric definitions and caveats, use and curate agent tooling for trustworthy answers, and contribute small data platform and pipeline improvements.

Build curated datasets and semantic models in Snowflake, perform forecasting and scenario analysis, and develop Power BI dashboards and reports. Automate reporting, maintain data pipelines, test data models, troubleshoot issues, and ensure consistent analytics standards across regions. The role also supports business stakeholders through ad hoc reporting guidance, training, and best practices in analytics and visualization.

Lead the billing analytics function: build and maintain billing data pipelines and dashboards, analyze payments/renewals/refunds/disputes, run experiments and forecasting, reduce churn and disputes, and manage/mentor a team of analysts while partnering with Billing, Support, Finance, Product, and Marketing.

Analyze billing and support performance, build and maintain data pipelines, apply statistical forecasting and A/B testing, collaborate cross-functionally, document analyses, and provide recommendations to reduce financial risk, disputes, churn, and operational inefficiencies.

Support monitoring, validation, and maintenance of spectroscopy-based authentication models (NIR mandatory). Interpret model performance, create model snapshots and metadata checks, collaborate with Data Scientists, contribute to validation and release, and document methods and changes for regulated environments.

Support Enterprise Risk Management through risk data collection, analysis, reporting automation, monitoring of key risk indicators, and management presentations. Develop and maintain Excel-based reporting processes and data transfer scripts, collaborate with stakeholders to validate business data, and identify reporting improvements. Assist with incident management, business continuity planning, and operational risk lessons learned. Translate complex risk topics into clear communications for varied audiences.

Analyze large-scale user and product data, design and evaluate A/B tests, define and validate product and business metrics, build dashboards and reports, and partner with engineers, data scientists, product, and customers to translate insights into product improvements.

The Senior Data Analyst will apply analytics to business problems, developing data products and collaborating with teams to deliver solutions while maintaining quality code.

Use SQL and Python to analyse transaction monitoring and fraud rule performance, identify patterns and anomalies, reduce false positives, and prioritise high-risk cases. Partner with FinCrime, Product, Compliance and Data teams to improve controls, data pipelines and detection logic, support scoring and future ML use cases, and translate analysis into actionable recommendations that balance fraud prevention with customer friction.

Serve as a techno-functional senior analyst translating business needs into requirements and user stories, validating data via SQL, collaborating with engineering and product teams to deliver scalable analytics, supporting UAT, driving adoption, and improving data product maturity across enterprise domains.