Lead Data Engineer

EPAM Systems; Argentina; Remote; ARS 1,200,000 - 1,900,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off

Upskilling & certifications

LinkedIn Learning access

Job summary

EPAM Systems seeks a Lead Data Engineer to strengthen our data platforms and enterprise pipelines. You will own end-to-end data ingestion, schema governance, and provenance-enabled processing across multiple source systems and projects.

You will design and implement adapters, mappers, resolvers, and signal catalogs to ensure accurate, auditable data flows for complex analytics and AI initiatives. Strong teamwork and English communication required.

Qualifications

• 5+ years of Python software development experience.
• Minimum 1 year leading or managing development teams.
• Hands-on AI data engineering for AI/ML-driven systems.
• Experience with Apache Airflow to schedule data workflows.
• PostgreSQL storage and querying experience.
• Knowledge of data quality, telemetry, and reconciliation.

Responsibilities

• Migrate databases across Unified Data Store preserving schema alignment.
• Develop source adapters linking external systems to the data platform.
• Build Field Mapper and standardize source data into unified schema.
• Create Link Resolver for cross-entity relationships and routing.
• Develop Attribution Resolver and Counted Signals Aggregator with provenance.
• Assemble Area Vocabulary and Translation Tables for uniform categorization.
• Implement Signal Catalogue jobs with eight core signals and provenance tracking.
• Maintain data dictionary for all storage elements as functionality evolves.

Skills

Python

Team leadership

AI Data Engineering

Apache Airflow

PostgreSQL

Data quality

Telemetry

Spec Driven Development

English communication

Tools

Airflow

PostgreSQL

Snowflake connectors

Jira REST API

QMetry / Zephyr

Code Hub / Perforce

Job description

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.ResponsibilitiesCarry out every database migration set across the Unified Data Store so that schema alignment remains intact throughout the platformConstruct and sustain the complete collection of Source Adapters that link outside systems into the broader data platformBuild out the Field Mapper, setting up field bindings specific to each project and franchise that standardize source system data into the unified schema upon intakeDevelop 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 ResolverConstruct 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 validationAssemble the Area Vocabulary along with its related Translation Tables to maintain uniform area categorization throughout the systemCreate 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 eachTake charge of writing the data dictionary for every storage element, updating it progressively as new functionality rolls outRequirementsFive-plus years of direct, practical experience in Python software developmentA minimum of one year spent leading or managing development teamsPrior 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 workflowsFunctional understanding of Machine Learning principles and how they integrate into data systemsDemonstrated capability in building data pipelines from initial design through full deploymentDirect experience working with PostgreSQL for both storing and querying dataBackground creating idempotent ingestion mechanisms, covering natural keys, upsert auditing, duplicate identification, and safe replay handlingExperience bringing together various heterogeneous data sources into one cohesive systemCompetency in data quality and telemetry approaches, including fill-rate tracking, volume measurement, and reconciliation reportingExposure to the Spec Driven Development approachReasonable communication skills combined with working English fluency (B2 level or above) to interpret business needs and convert them into agentic architecturesNice to haveBackground building API clients for platforms like Perforce or Code HubExperience connecting with the JaaS (Jira) REST APIExposure to test management APIs such as QMetry, Zephyr, or comparable solutionsBackground developing Snowflake connectors, covering key-pair authentication and warehouse extract queryingUnderstanding of temporal data systems along with as-of read patternsWe offerInternational projects with top brandsWork with global teams of highly skilled, diverse peersHealthcare benefitsEmployee financial programsPaid time off and sick leaveUpskilling, reskilling and certification coursesUnlimited access to the LinkedIn Learning library and 22,000+ coursesGlobal career opportunitiesVolunteer and community involvement opportunitiesEPAM Employee GroupsAward-winning culture recognized by Glassdoor, Newsweek and LinkedIn