Data Analyst (Remote)

• Data Extraction and Standardization: Lead initiatives to extract and standardize financial data from various formats, including PDF, HTML, XBRL and iXBRL, ensuring data accuracy and consistency.
• Exploratory Data Analysis: Conduct exploratory data analysis to identify trends, raise important questions, and derive actionable insights.
• Data Model development: 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.
• Data Model Democratization: Partner with data engineering teams to democratize the data model for designing efficient data pipelines.
• Data Modelling Standards: Define and enforce data modelling standards and best practices..
• Programming and Automation: 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.
• Database Management: Manage and optimize databases (SQL Server, Neo4j, Snowflake, Postgres), understanding join types, aggregate functions, and data storage formats (Parquet, AVRO, Delta).

Highly skilled in data modelling: Experience developing data models from scratch for green field projects in multiple domains. Should have Deep understanding of data warehousing concepts, dimensional modelling, and normalization/denormalization techniques. Expertise in tools such as Erwin Data Modeler, PowerDesigner, or similar.

• Knowledge of data products: Strong understanding of data product design principles and lifecycle.
• Strong SQL skills and experience with relational (e.g., Oracle, SQL Server, PostgreSQL) and cloud databases (e.g., Snowflake, BigQuery, Redshift).
• Good understanding of Azure cloud data services (Data Lake, Data Factory, Azure SQL).
• Experience with Databases and Data Formats: Familiar with various databases, operating systems, file types, and data formats.

Advanced Data Modelling Techniques: Experience with advanced modelling.

• Business Analysis Expertise: Ability to bridge the gap between technical and business requirements.
• Exposure to Programming: Skilled in Python, Spark and SQL.
• Project Management: Skills in managing projects, timelines, and deliverables.
• Data Visualization Tools: Proficiency with tools such as Tableau, Power BI, or similar.