Senior Data Engineer

About the Position

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

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

Locations

• Argentina
• Brazil

Work Location Type

• Hybrid