We're seeking a Lead Data Engineer to come aboard and strengthen our team.

Among all engineering positions tied to this engagement, this one carries the heaviest data responsibility.

Should any pipeline lose data, positions shift out of alignment, or signals miscalculate, the entire calibration and UAT process collapses.

Nothing else can function properly unless accuracy and operational resilience are locked in here first.

Responsibilities

Carry out every database migration set across the Unified Data Store so that schema alignment remains intact throughout the platform

Construct and sustain the complete collection of Source Adapters that link outside systems into the broader data platform

Build out the Field Mapper, setting up field bindings specific to each project and franchise that standardize source system data into the unified schema upon intake

Develop the Link Resolver, tasked with connecting CL-to-ticket, ticket-to-ticket, and case-to-defect/requirement relationships spanning every source family, supplying input directly to the Attribution Resolver

Construct the Attribution Resolver to map the change-ticket-area-test pathway, together with the Counted Signals Aggregator, which generates file-to-area and area-to-area association tables complete with path standardization and count validation

Assemble the Area Vocabulary along with its related Translation Tables to maintain uniform area categorization throughout the system

Create the complete set of Signal Catalogue computation jobs addressing all eight primary signals — area fragility, recency, recent failures, change-touch, coupling, windowed area change volume, testing alignment, validation recency, and defect impact/volume/age — with provenance tracking built into each

Take charge of writing the data dictionary for every storage element, updating it progressively as new functionality rolls out

Requirements

Five-plus years of direct,



practical experience in Python software developmentA minimum of one year spent leading or managing development teams

Prior exposure to AI Data Engineering, applying relevant practices to support systems driven by AI/MLReal-world experience leveraging Apache Airflow to coordinate and schedule data-related workflows

Functional understanding of Machine Learning principles and how they integrate into data systems

Demonstrated capability in building data pipelines from initial design through full deployment

Direct experience working with PostgreSQL for both storing and querying data

Background creating idempotent ingestion mechanisms, covering natural keys, upsert auditing, duplicate identification, and safe replay handling

Experience bringing together various heterogeneous data sources into one cohesive system

Competency in data quality and telemetry approaches, including fill-rate tracking, volume measurement, and reconciliation reporting

Exposure to the Spec Driven Development approach

Reasonable communication skills combined with working English fluency (B2 level or above) to interpret business needs and convert them into agentic architectures

Nice to have

Background building API clients for platforms like Perforce or Code Hub

Experience connecting with the JaaS (Jira) REST APIExposure to test management APIs such as QMetry, Zephyr, or comparable solutions

Background developing Snowflake connectors, covering key-pair authentication and warehouse extract querying

Understanding of temporal data systems along with as-of read patterns

We offer

International projects with top brands

Work with general 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

General career opportunities

Volunteer and community involvement opportunitiesEPAM Employee Groups

Award-winning culture recognized by Glassdoor, Newsweek and Linked

In #J-18808-Ljbffr

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