Data Engineer

• Design module content for the Fintech Engineering Specialization at Jala University
• Create labs
• Develop a reference implementation
• Build an evaluation harness
• Prepare an instructor guide

Academic Content Developer – Financial Data Engineering, Credit Systems

• Support new and existing customers with their data engineering needs
• Guide customers in making technical decisions to achieve their goals
• Work across multiple customer accounts and track and report progress
• Design, build, and operationalize complex data solutions
• Fix issues, apply transformations, and recommend data cleansing and quality solutions
• Analyze data sources and recommend their inclusion in analytical processes
• Incorporate data governance, data security, and data quality practices
• Collaborate across teams to support delivery and educate end users on data products and analytic environments
• Perform data and system analysis, assessment, and resolution for defects and incidents
• Test data movement, transformation code, and data components

Senior Data Engineer

• Own the inventory of every credit data source, including TransUnion, Clarity, Plaid, Prism, and internal data from the app and loan servicing
• Define required data, grain, freshness, and destination for each source
• Write data contracts with engineers building vendor integrations
• Confirm raw vendor responses are stored completely for model development and audits
• Monitor freshness, volume, fill rate, and schema for every source and model input
• Set up alerts for feature fill-rate drops, missing vendor fields, and volume deviations
• Track input drift for production models alongside data scientists
• Triage data incidents, identify root causes, route fixes, and track closure
• Own the Credit Risk layer of the Snowflake warehouse, including raw, staging, mart, and feature tables
• Build and maintain pipelines and transformations for application, loan, performance, and vendor data
• Keep modeling datasets reproducible and prevent training-data leakage
• Maintain tables behind Credit Risk dashboards and governed metrics
• Write automated tests for keys, duplicates, ranges, referential integrity, and source reconciliation
• Maintain documentation, lineage, and owner registries
• Support vendor oversight by checking SLAs and reconciling pull counts against vendor invoices

Credit Risk Data Engineer

• Construct resilient Python data processing pipelines using Medallion architecture principles (Bronze/Silver/Gold)
• Transform raw data into analytics-ready assets
• Standardize data storage using Parquet, Apache Iceberg, and Delta Lake
• Leverage DuckDB with Python for localized data processing within ETL workflows
• Architect and optimize production PostgreSQL schemas
• Utilize SQLite for embedded or lightweight localized storage
• Scale ETL, data transformation, and analytics workloads using Databricks or Apache Spark

Data Engineer – Core, Analytics

• Design and operate the data foundation supporting AI systems
• Own movement, modeling, and quality of data from source systems through the warehouse into retrieval and feature layers
• Build and maintain data pipelines powering LLM pipelines, agentic workflows, and analytical products
• Model warehouse schemas and make decisions about grain, relationships, and denormalization
• Write production Python and maintain pipelines as versioned, tested, observable software
• Partner with the AI/ML Data Scientist, who owns model behavior and retrieval strategy
• Own the pipeline, schema, and data guarantees while collaborating on the algorithm, prompt, and evaluation boundary

Senior AI Data Engineer

• Own the architecture and technical roadmap for key pieces of Jampp's data infrastructure
• Design and implement scalable, incremental, and maintainable ETL processes from varied sources
• Partner with Data Scientists and Senior/Staff engineers on scalable data models and pipelines supporting real-time ML infrastructure
• Drive data consistency and reliability standards across systems and machine learning inputs
• Identify and resolve systemic bottlenecks through benchmarking, root-cause diagnosis, and architectural solutions
• Build and evolve tools and frameworks enabling company-wide data access and reliable, scalable transformations
• Mentor and coach mid-level and junior engineers
• Contribute to technical decisions about tooling and infrastructure choices

• Develop, optimize, and maintain secure HR data pipelines using dbt, PySpark, Python, and Apache Airflow
• Ingest HR data from REST APIs, real-time streams, Google Sheets, web APIs, and external platforms
• Provision, configure, and maintain scalable extraction, transformation, and loading infrastructure using Terraform, AWS Glue, AWS EMR, and AWS S3
• Design and maintain data models, data marts, and warehouse structures in Snowflake and Google BigQuery
• Build semantic views and ontology layers mapping CORE → SEMANTIC → METRICS on dbt models
• Configure and deploy generative AI query agents using Snowflake Cortex Analyst and Cortex Search
• Maintain MCP connections between Snowflake and internal AI tools
• Monitor pipelines to enforce 99.5% uptime
• Build automated tests within a Data Quality Framework
• Ensure secure handling of PII and produce comprehensive technical documentation
• Support workforce analytics for international enterprise Centers of Excellence and executive leadership

• Design, implement, test, deploy, and operate production services for ingestion, normalization, enrichment, identity resolution, scoring, and intelligence delivery
• Build maintainable pipeline workers, event consumers, APIs, scheduled processes, and supporting libraries
• Own the complete software lifecycle, including architecture, implementation, testing, deployment, monitoring, incident response, and improvement
• Establish reusable engineering patterns for the team
• Develop asynchronous workflows with explicit data, event, and work contracts
• Design for duplicate delivery, ordering constraints, idempotency, retries, timeouts, partial failure, dead-letter handling, backpressure, and recovery
• Make pipeline state and failures observable, with reconciliation for missing, delayed, duplicated, or inconsistent processing
• Support safe replay and reprocessing without silently changing historical-result meaning
• Preserve source evidence, provenance, lineage, processing context, and applicable versions
• Define validation and quality controls at service boundaries and safely evolve schemas, contracts, and processing logic
• Preserve tenant isolation while combining shared intelligence with customer-private evidence, configuration, and conclusions
• Apply authorization, retention, deletion, audit, GDPR, and SOC 2 requirements throughout data-processing workflows
• Evaluate managed services, open-source components, existing capabilities, and purpose-built services based on product and operational requirements
• Contribute to architecture through working software, written proposals, prototypes, and technical review
• Collaborate with Product, Platform Engineering, Threat Research, Data Science, Security, and customer-facing teams
• Translate product requirements into technical contracts, communicate tradeoffs, and mentor engineers in modern data and distributed-systems practices

Senior Data Engineer, Product Data Systems

• Design and build scalable backend systems using Java
• Lead the architecture and implementation of distributed systems across complex multi-service environments handling large-scale data processing
• Develop and optimize concurrent and multi-threaded applications
• Build and maintain big data pipelines and infrastructure for high-volume data ingestion, processing, and storage
• Manage containerized workloads using Kubernetes, including deployment strategies, scaling policies, and resource management
• Mentor junior and mid-level engineers on backend best practices, system design, and performance optimization
• Collaborate with cross-functional teams to define service contracts, APIs, and integration patterns
• Implement observability and monitoring solutions
• Support production deployments, troubleshoot complex issues, and drive continuous improvement initiatives

Lead Backend Data Engineer

• Diseñar y evolucionar la arquitectura de datos de la organización
• Definir arquitecturas para Data Lake, Data Warehouse, Lakehouse y plataformas analíticas
• Diseñar estrategias de integración entre diferentes fuentes y sistemas
• Definir patrones y estándares para la ingesta, transformación, almacenamiento y consumo de datos
• Diseñar modelos de datos conceptuales, lógicos y físicos
• Definir estrategias de almacenamiento y procesamiento de grandes volúmenes de información
• Evaluar y seleccionar tecnologías y herramientas de datos según las necesidades del negocio
• Diseñar soluciones considerando escalabilidad, disponibilidad, performance, seguridad y costos
• Definir lineamientos de Data Governance, Data Quality, Metadata y Data Lineage
• Trabajar sobre políticas de acceso, privacidad y seguridad de los datos
• Establecer estándares y buenas prácticas para equipos de Data Engineering y Analytics
• Revisar y acompañar decisiones técnicas relacionadas con plataformas y productos de datos
• Identificar oportunidades de mejora y modernización de la arquitectura existente
• Participar en la definición de roadmaps tecnológicos y evolución de la plataforma de datos
• Documentar arquitecturas, decisiones técnicas, estándares y principios de diseño
• Acompañar técnicamente a los equipos durante la implementación de soluciones

Data Engineer jobs in Argentina

Academic Content Developer – Financial Data Engineering, Credit Systems

Senior Data Engineer

Credit Risk Data Engineer

Data Engineer – Core, Analytics

Senior AI Data Engineer

Senior Data Engineer

Data Engineer

Senior Data Engineer, Product Data Systems

Lead Backend Data Engineer

Data Architect