Data Analyst

Responsibilities:

• 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).

Skills and attributes for success

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.

Preferred Skills: 

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.