Data analyst

apreel Sp. z o.o. offers a remote data analyst position focusing on data modelling, data product design, and cloud data services.

• Highly skilled in data modelling with experience developing data models from scratch for green field projects in multiple domains.
• Deep understanding of data warehousing concepts, dimensional modelling, normalization/denormalization techniques.
• Expertise in tools such as Erwin Data Modeler, PowerDesigner, or similar.
• Strong understanding of data product design principles and lifecycle.
• Strong SQL skills and experience with relational databases (Oracle, SQL Server, PostgreSQL) and cloud databases (Snowflake, BigQuery, Redshift).
• Good understanding of Azure cloud data services including Data Lake, Data Factory, and Azure SQL.
• Problem-solving skills and ability to work with ambiguity.
• Effective communication skills for conveying complex ideas and collaborating with stakeholders.
• Familiarity with various databases, operating systems, file types, and data formats.
• Experience with different data roles including analysis, modelling, and science.

Nice to have:

• Advanced data modelling techniques.
• Business analysis expertise to bridge technical and business requirements.
• Exposure to programming in Python, Spark, and SQL.
• Project management skills.
• Proficiency with data visualization tools such as Tableau and Power BI.

• Lead initiatives to extract and standardize financial data from various formats including PDF, HTML, XBRL, and iXBRL, ensuring data accuracy and consistency.
• Provide guidance and support to junior analysts in data analysis, programming, and statistical methodologies.
• Conduct exploratory data analysis to identify trends, raise questions, and derive actionable insights.
• Design, implement, and optimize conceptual, logical, and physical data models for enterprise-scale data products.
• Develop and maintain data models using ERD diagrams and manage data dictionaries for various schema types.
• Partner with data engineering teams to democratize data models for efficient data pipeline design.
• Define and enforce data modelling standards and best practices; validate compliance and data quality.
• Design custom taxonomies and reference data classification methods/structures.
• Utilize programming languages such as Python, Spark, Regex, Shell Scripts, and SQL for data manipulation, analysis, and automation.
• Manage and optimize databases including SQL Server, Neo4j, Snowflake, and Postgres.
• Collaborate with product managers, data engineers, and analysts to translate business requirements into robust data structures.
• Work closely with cross-functional teams to address data quality issues and implement solutions.
• Use tools such as Jira, Confluence, GIT, Agile, and Scrum for project management and collaboration.