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Ref: #73661
Data Analyst
Your key 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.
- Mentorship and Development: Provide guidance and support to junior analysts, fostering their growth in data analysis, programming, and statistical methodologies.
- 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. Conduct data analysis to validate modelling standard compliance, model accuracy, identify anomalies, and ensure data quality.
- Data Classification and Taxonomies: Design custom taxonomies, and reference data classification methods/structures
- 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).
- Collaboration: 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 effective solutions, promoting a culture of continuous improvement.
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).
- Problem-Solving: Adept at tackling complex issues and finding effective solutions.
- Curiosity and Self-Starter: Always eager to learn and take initiative without needing constant guidance.
- Comfortable with Ambiguity: Capable of working efficiently even when the answers are not immediately clear.
- Effective Communication: Excellent at conveying complex ideas and collaborating with stakeholders.
- Experience with Databases and Data Formats: Familiar with various databases, operating systems, file types, and data formats.
- Experience with different data roles (analysis, modelling, science, etc).
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.
Qualifications:
- Bachelor’s degree in computer science, Information Technology, or equivalent
- 5+ years of experience in data management
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