Lead Data Integration Engineer

EPAM Systems; Argentina; On-site; ARS 1,800,000 - 3,000,000; Full time

Benefits offered by this job

Healthcare benefits

Global career opportunities

Upskilling and certification programs

Paid time off and sick leave

Job summary

EPAM Systems in Argentina seeks a Lead Data Integration Engineer to build and maintain production data models in dbt and Snowflake, unifying Salesforce and Gong-style data into trusted semantic layers for GTM analytics and AI agents.

You will prepare datasets for Snowflake Cortex and Intelligence, define patterns, and enable self-service data modeling for analysts and product teams in a fast-paced, global environment.

Qualifications

• 5+ years in analytics engineering, data engineering, or data architecture.
• Proficiency in advanced SQL with window functions and analytic queries.
• 5+ years of production dbt work with modular projects, tests, and snapshots.
• Hands-on Snowflake expertise: virtual warehouses, Time Travel, and access control.
• Modeling B2B SaaS data from Salesforce, Gong, Marketo, or billing sources.
• Strong knowledge of Kimball dimensional modeling and semantic layer design.
• Experience with Git workflows and CI testing.
• Willingness to use AI-assisted development tools for faster modeling and testing.
• English at Upper-Intermediate level or higher.

Responsibilities

• Design production data models in dbt and Snowflake to measure account health and customer signals.
• Integrate structured and unstructured data into reliable account-level dimensions.
• Define consistent modeling patterns and metrics across GTM datasets.
• Operate a semantic layer to prevent conflicting metric calculations across tools.
• Prepare datasets for Snowflake Cortex, Snowflake Intelligence, and AI agents.
• Create feature tables and context stores for LLM-assisted workflows.
• Implement data governance, RBAC, and audit trails across sensitive sales data.
• Collaborate with Revenue Operations and governance teams on data lineage.
• Contribute to agile sprints and design reviews.
• Deliver self-service data models and documentation for analysts.

Skills

SQL proficiency

dbt

Snowflake

B2B SaaS data modeling

Kimball dimensional modeling

Git workflows

AI-assisted development

English proficiency

Tools

Snowflake

dbt

Job description

We are seeking a Lead Data Integration Engineer to deliver production dbt and Snowflake models powering GTM analytics and AI agents. You will create trusted semantic layers, unify Salesforce and Gong-style interaction data, and prepare datasets for Snowflake Cortex and Snowflake Intelligence.

Responsibilities

• Design and maintain production data models in dbt and Snowflake to measure account health, engagement velocity, and customer intent signals
• Integrate structured records with unstructured interaction data (Gong call transcripts, emails, Salesforce CRM objects, and product usage telemetry) into reliable account-level dimensions
• Define consistent modeling patterns, naming conventions, and metric definitions across GTM datasets
• Implement and operate an enterprise semantic layer (such as the dbt Semantic Layer or MetricFlow) to prevent conflicting metric calculations across business tools
• Prepare clean, context-rich datasets optimized for retrieval, search, and reasoning in Snowflake Cortex, Snowflake Intelligence, and internal AI agents
• Create feature tables and context stores that enable LLM agents for automated meeting prep, competitive intelligence, and account research
• Administer metadata and data enrichment patterns so AI agents correctly interpret account stages, customer sentiment, and relationship history
• Develop automated tests, schema validations, and freshness monitors in dbt for both structured metrics and unstructured data transformations
• Enforce data governance, role-based access controls (RBAC), and audit trails across sensitive sales pipeline and customer interaction data
• Collaborate with Revenue Operations and governance teams to document data lineage and maintain clear dataset ownership
• Contribute within an agile sprint cadence, supporting backlog planning, design reviews, and technical standards
• Deliver self-service data models and documentation so analysts and product teams can query data independently

Requirements

• Proven track record with 5+ years of commercial experience in analytics engineering, data engineering, or data architecture roles
• Expert-level proficiency in advanced SQL, including window functions, CTEs, complex joins, analytical queries, and execution plan optimization
• Extensive hands-on experience with 5+ years of production dbt work, including modular project structure, Jinja macros, custom tests, incremental models, and snapshots
• Solid practical experience with Snowflake, including virtual warehouse sizing, query profile analysis, clustering keys, Time Travel, zero-copy cloning, and access control
• Demonstrated experience modeling B2B SaaS revenue and interaction data (Salesforce CRM, Gong, Marketo, or subscription billing data)
• Deep understanding of Kimball dimensional modeling (star schemas, slowly changing dimensions SCD 1/2/4, conformed dimensions) and semantic layer design
• Daily comfort with Git workflows, branching strategies, code reviews, and automated CI test runs
• Willingness to use AI-assisted development tools (GitHub Copilot, Cursor, Claude Code) to accelerate modeling work, test coverage, and documentation
• English proficiency at B2 (Upper-Intermediate) level or higher

Nice to have

• Working familiarity with Snowflake Cortex AI functions (Cortex Search, LLM functions, embeddings)
• Understanding of vector embeddings, chunking strategies, and retrieval-augmented generation (RAG) for conversational text
• Working knowledge of Python for data manipulation (Pandas), API extraction, or pipeline automation
• Experience with metadata and lineage tools such as Alation, Collibra, DataHub, or dbt Docs
• Background designing self-service data models for Tableau, Looker, or Power BI

We offer

• International projects with top brands
• Work with global teams of highly skilled, diverse peers
• Healthcare benefits
• Employee financial programs
• Paid time off and sick leave
• Upskilling, reskilling and certification courses
• Unlimited access to the LinkedIn Learning library and 22,000+ courses
• Global career opportunities
• Volunteer and community involvement opportunities
• EPAM Employee Groups
• Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn