Senior Data Engineer at EPAM Systems – Argentina | Jobyssey

We are looking for a Senior Data Engineer to join our team. The Data Engineer is the most data-intensive engineering role on the engagement. If any pipeline drops data, positions drift, or signals compute incorrectly, calibration and UAT break. Correctness and operational robustness here are a prerequisite for everything else.

Responsibilities

Execute all database migration sets spanning the Unified Data Store to ensure schema consistency across the platform
Build and maintain the full suite of Source Adapters responsible for connecting external systems into the data platform
Implement the Field Mapper, establishing per-project and per-franchise field bindings that normalize source system fields into the unified schema during ingestion
Implement the Link Resolver, responsible for resolving CL-to-ticket, ticket-to-ticket, and case-to-defect/requirement links across all source families, feeding directly into the Attribution Resolver
Implement the Attribution Resolver, tracing the change-to-ticket-to-area-to-test chain, alongside the Counted Signals Aggregator, which builds file-to-area and area-to-area counted association tables with path normalization and counting verification
Build the Area Vocabulary and accompanying Translation Tables to support consistent area classification across the system
Develop all Signal Catalogue computation jobs covering the eight core signals — area fragility, recency, recent failures, change-touch, coupling, windowed area change volume, testing alignment, validation recency, and defect impact/volume/age — ensuring provenance capture throughout
Take ownership of data dictionary authoring across all storage components, documenting incrementally as new features are delivered

Requirements

3+ years of hands-on relevant experience in Python software engineering
Background in AI Data Engineering, applying data engineering practices to support AI/ML-driven systems
Practical experience with Apache Airflow for orchestrating and scheduling data workflows
Working knowledge of Machine Learning concepts and their application within data systems
Proven experience in data pipeline development, from design through implementation
Hands-on experience with PostgreSQL for data storage and querying
Experience designing idempotent ingest processes, including natural keys, upsert auditing, duplicate detection, and replay safety
Experience integrating multiple heterogeneous data sources into a unified system
Skilled in data quality and telemetry practices, including fill-rate counters, volume metrics, and reconciliation reporting
Familiarity with Spec Driven Development methodology
Decent communication skills with working English fluency (B2 level or higher) to understand business requirements and translate them into agentic architectures

Nice to have

Experience developing API clients for Perforce or Code Hub
Experience integrating with the JaaS (Jira) REST API
Familiarity with test management APIs such as QMetry, Zephyr, or similar tools
Experience building Snowflake connectors, including key-pair authentication and warehouse extract queries
Knowledge of temporal data systems and as-of read patterns

Benefits

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

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Responsibilities

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