Lead Data Integration Engineer for AI-Driven Analytics

EPAM Systems, Inc.; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

EPAM Systems, Inc. seeks a Lead Data Integration Engineer to lead analytics engineering and data modeling turning raw interaction and telemetry data into trusted account datasets. You will partner with cross-functional teams to deliver governed, self-service data products in Snowflake and dbt.

You design dimensional models, build dbt models, establish naming conventions, and drive AI-ready data workflows while ensuring governance and auditability across pipelines.

Qualifications

• 5+ years analytics engineering or data engineering experience.
• Data modeling for BI analytics and dimensional schemas.
• Strong leadership to define modeling standards and reviews.

Responsibilities

• Lead analytics engineering delivery for integrated account-level datasets.
• Design dimensional models and semantic definitions for metrics.
• Build dbt models in Snowflake transforming inputs to consistent facts.
• Establish naming conventions and shared metric logic to avoid conflicts.
• Create datasets for AI agent workflows and search use cases.
• Develop feature tables and context stores for account research.
• Implement dbt tests, schema validations, and freshness monitoring.
• Coordinate governance, RBAC, and data lineage with partners.
• Deliver self-service models with clear docs for analysts and product teams.

Skills

Analytics engineering

Data modeling

Leadership

SQL

dbt

Snowflake

Git

Stakeholder collaboration

English proficiency

Tools

Snowflake

dbt

Git

Tableau/Power BI

Job description

EPAM Systems, Inc. seeks a Lead Data Integration Engineer to lead analytics engineering and data modeling turning raw interaction and telemetry data into trusted account datasets. You will partner with cross-functional teams to deliver governed, self-service data products in Snowflake and dbt.

You design dimensional models, build dbt models, establish naming conventions, and drive AI-ready data workflows while ensuring governance and auditability across pipelines.