Lead Data Engineer

Hybrid in Argentina, Brazil

Data Integration

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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 development
A 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/ML
Real-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 API
Exposure 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