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

Requirements3+ 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 APIFamiliarity 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

We offer

International projects with top brands

Work with integral 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 Linked

In Learning library and 22,000+ courses

Global career opportunities

Volunteer and community involvement opportunitiesEPAM Employee Groups

Award-winning culture recognized by Glassdoor, Newsweek and Linked

In #J-18808-Ljbffr

📌 Senior Data Engineer (Argentina)
🏢 EPAM Systems
📍 Argentina