Data Analyst

Job Summary

The Data Analyst will analyze massive internal datasets to uncover trends, optimize database query architectures, and write automated Python scripts to clean and manipulate data streams. Key functions include engineering advanced PL/SQL transformation logic, creating structured reports that serve as gold-standard inputs for AI models, verifying data integrity across multiple source nodes, and documenting technical methodologies with exceptional clarity.

Qualification

Candidates must meet the following baseline educational and environmental standards:

• Required Degree: Bachelor’s degree in Computer Science, Data Science, Statistics, or a closely related quantitative discipline.
• Remote Independence: A proven ability to remain highly productive, manage time effectively, and maintain strict deadlines within a completely independent, remote workspace.
• Linguistic Excellence: Outstanding written and verbal English communication skills, specifically demonstrating a talent for breaking down intricate data science anomalies for non-technical project orchestrators.

Experience

Candidates must demonstrate a robust background in relational databases, data transformation pipelines, and automated scripting:

Technical Analytics Mastery

• Professional Tenacity: 3 to 5 years of proven professional experience operating as a functional Data Analyst, Data Engineer, or in an equivalent data-intensive analytical role.
• Advanced Python Scripting: High-level proficiency using Python for automated data cleaning, data profiling, parsing complex file formats, and executing advanced statistical analysis.
• Expert-Level PL/SQL: Deep, hands-on mastery of PL/SQL (Procedural Language for SQL) for engineering complex queries, managing stored procedures, executing data transformations (ETL), and manipulating relational databases.
• Data Quality Assurance: Proven track record of auditing information systems to ensure absolute data integrity, consistency, and structural alignment across disparate tracking sources.

Visualization & Platforms (Preferred)

• Cloud Infrastructure Fluency: Practical experience navigating cloud-based data warehouses or enterprise platforms (e.g., AWS, Snowflake, Google Cloud Platform).
• Business Intelligence Tooling: Familiarity with configuring visually rich, scannable data visualization dashboards using industry-standard tools like Tableau or Power BI.
• Methodology Documentation: Experience writing detailed, scannable technical documentation, process maps, or methodology write-ups with meticulous attention to structural detail.
• Cross-Functional Influence: A history of supporting cross-functional engineering or product teams to drive data-informed project decisions.
• Professional Credentials: Active industry certifications in data analytics, database administration, or advanced Python structures are highly valued.

Key Responsibilities

Advanced Data Extraction & Automation

• Optimize Database Communications: Develop, refine, and maintain advanced PL/SQL queries and procedures to extract, filter, and transform massive data fragments efficiently.
• Automate Analytics Pipelines: Utilize Python scripting to automate repetitive data extraction cycles, remove corrupted inputs, and run automated data quality validations.
• Deconstruct Large Datasets: Analyze large, multi-layered datasets to uncover underlying systemic trends, behavioral patterns, and actionable technical insights.

AI Training & Feedback Loop Engineering

• Generate Model Training Inputs: Shape how next-generation AI models learn by crafting comprehensive, high-quality data readouts and logical inputs that serve as gold-standard reasoning blueprints.
• Evaluate AI Reasoning: Audit, test, and provide domain-specific corrections to data analytics answers generated by autonomous AI agents to ensure real-world business accuracy.
• Maintain Data Integrity: Continuously audit ingestion streams to verify data integrity, preventing corrupt or inconsistent data records from entering the core AI training models.

Cross-Functional Collaboration & Documentation

• Partner Globally: Collaborate asynchronously with remote, cross-functional engineering cells to accurately define data requirements and deliver data-driven solutions.
• Author Meticulous Documentation: Log data processing steps, database architectures, and analytical methodologies with exceptional attention to detail to ensure reproducibility.
• Communicate Strategic Insights: Craft clear data reports and visual frameworks to communicate analytical findings clearly to both deep-tech developers and non-technical stakeholders.

Skills Required:

• Computer / Software / It / Data