Design, develop, and maintain scalable data pipelines for batch and near-real-time use cases
Build and optimize Snowflake architecture, including schema design, data modeling, performance tuning, and warehouse optimization
Integrate data from APIs, databases, SaaS platforms, and enterprise systems into a unified data warehouse
Implement and manage ETL/ELT workflows using tools such as dbt, Airflow, or equivalent orchestration frameworks
Ensure data quality, reliability, governance, and proper documentation across the data lifecycle
Develop executive-level dashboards and reports using Tableau and/or Power BI
Translate business requirements into scalable data models and analytics-ready datasets
Analyze complex datasets to identify trends, performance drivers, and operational insights
Optimize dashboard performance, usability, and governance standards
Train business users and promote adoption of BI solutions across the organization
Partner with analysts, AI engineers, delivery managers, and business stakeholders to align data architecture with strategic objectives
Support AI and advanced analytics initiatives by providing clean, curated, and well-structured datasets
Contribute to best practices in data engineering, analytics standards, and platform governance

4–7 years of experience in Data Engineering, BI Development, or analytics engineering roles
Strong hands-on experience with Snowflake (schema design, performance tuning, warehousing concepts)
Advanced SQL proficiency and solid Python skills for data transformation and pipeline development
Experience with dbt, Airflow, or similar ETL/ELT orchestration tools
Strong experience with Tableau and/or Power BI for dashboard and visualization development
Solid understanding of data warehousing concepts, dimensional modeling, and analytics architecture
Experience working in cloud environments (AWS, Azure, or GCP)
Strong analytical mindset with the ability to translate business needs into scalable technical solutions
Excellent communication skills and comfort working in consulting-style environments
Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field preferred.

The opportunity to work at the forefront of AI-powered enterprise data transformation.
Exposure to private equity-backed organizations undergoing large-scale modernization.
A high-impact, high-autonomy environment where ownership and measurable outcomes are valued.
Collaboration with global experts across data, AI, and enterprise systems.
Professional development and certification support (Snowflake, Tableau, Power BI, dbt).
Flexible, remote-first work environment with performance-driven expectations.

Full Stack Data Engineer

Key skills