Job Description

Company Summary

Bloom AI is an AI Services firm that builds data to intelligence layer. We empower enterprises to unlock the value of data with human-like synthesis and decision intelligence at scale. Our proprietary tools and solutions are trusted by investment managers, insurance, private equity, and Fortune 1000 companies for more informed, efficient, and productive business practices.

Bloom AI is in Raleigh (U.S.) and New Delhi (India).

We are seeking a detail-oriented and driven Data Engineer (2–5 years experience) to support data engineering and analytics initiatives. The ideal candidate will have strong Python and SQL skills, experience building and maintaining data pipelines, and an interest in developing scalable data workflows

for analytics and reporting.

What you'll Do

Design, build, and maintain scalable data pipelines to ingest, transform, and integrate data from multiple sources such as web analytics platforms, campaign systems, and CRM tools.
Develop and optimize SQL-based data models, transformations, and queries to support analytics, reporting, and business intelligence.
Use Python for data processing, automation, and pipeline orchestration, improving efficiency and reliability of data workflows.
Ensure data quality, validation, and monitoring across the data pipeline lifecycle.
Collaborate with analytics consultants, marketing teams, and business stakeholders to deliver clean, reliable datasets for dashboards and insights.
Support data infrastructure improvements, including workflow automation, pipeline performance optimization, and process standardization.
Work with modern data platforms and warehouses such as Snowflake, Databricks, or similar environments when applicable.
Document pipeline architecture, transformations, and data quality processes to maintain transparency and maintainability.

What We’re Looking For

Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, or a related field.
2–5 years of experience in data engineering, analytics engineering, or data platform roles.
Strong hands-on experience with SQL and Python for data transformation, pipeline development, and automation.
Someone with AI exposure if a must.
Experience building and maintaining data pipelines and ETL/ELT workflows.
Familiarity with modern data platforms such as Snowflake, Databricks, BigQuery, or similar is a plus but not mandatory.
Experience working with structured and semi-structured data from APIs, marketing platforms, or enterprise systems.
Understanding of data modeling, pipeline monitoring, and data quality practices.
Strong problem-solving skills and ability to work collaboratively with both technical and business teams.

What You’ll Gain

Exposure to large-scale data infrastructure and analytics pipelines supporting global asset management clients.
Opportunities to work with modern cloud data platforms and scalable data workflows.
High-impact contributions to data engineering, analytics enablement, and automation initiatives.
A collaborative, mentorship-driven culture in a fast-growing global team.
Competitive compensation and benefits.