Data Analyst

Firma apreel powstała w kwietniu 2010 roku. W miarę rozwoju firmy i równolegle ze wzrostem poziomu zaufania klientów, jej działalność poszerzyła się o usługi Outsourcingu Specjalistów IT. Dziś to właśnie ten obszar stanowi główny filar działalności apreel.

• Experience developing data models from scratch for green field projects in multiple domains.
• Deep understanding of data warehousing concepts, dimensional modelling, and 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 (Oracle, SQL Server, PostgreSQL) and cloud databases (Snowflake, BigQuery, Redshift).
• Good understanding of Azure cloud data services (Data Lake, Data Factory, Azure SQL).
• Familiarity with various databases, operating systems, file types, and data formats.

Preferred Skills:

• Experience with advanced data modelling techniques.
• Business analysis expertise to bridge technical and business requirements.
• Skilled in Python, Spark and SQL programming.
• Project management skills.
• Proficiency with data visualization tools such as Tableau, Power BI, or similar.
• Minimum 3 years of experience in a similar position.
• Knowledge of Python, BigQuery, Azure, SQL.
• Languages: Polish, English.

• Lead initiatives to extract and standardize financial data from various formats, including PDF, HTML, XBRL and iXBRL, ensuring data accuracy and consistency.
• Conduct exploratory data analysis to identify trends, raise important 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 the data dictionaries, for transactional, star and flat schemas etc for different storage structures.
• Partner with data engineering teams to democratize the data model for designing efficient data pipelines.
• Define and enforce data modelling standards and best practices.
• Utilize programming languages such as Python, Spark, Regex, Shell Scripts and SQL for data manipulation, analysis, and automation of processes, including meta-programming and dynamic code generation.
• Manage and optimize databases (SQL Server, Neo4j, Snowflake, Postgres), understanding join types, aggregate functions, and data storage formats (Parquet, AVRO, Delta).