Azure fabric Data engineering

Globant is an IT and Software Development company operating in Argentina, Colombia, Uruguay, the United Kingdom, Brazil, the United States, Peru, India, Mexico, Chile, Spain, Romania and Belarus. It was formed in 2003 by Martín Migoya, Guibert Englebienne, Martín Umaran and Néstor Nocetti. It was founded in Buenos Aires, but currently is headquartered in Luxembourg and principally serves clients in the United States and United Kingdom.

We are seeking an Azure Data Engineer with strong, hands-on Microsoft Fabric experience to
build and operate a scalable middle-layer data and integration platform. The role focuses on
ingesting data from diverse sources, implementing transformation and business rules, and
delivering curated, consumption-ready datasets/models for analytics and downstream
applications.

Key Responsibilities
 Fabrics Implementation: Work on the fabrics platform to design and implement robust
data solutions, including One Lake architecture for efficient data storage and processing.
 Build & optimize data pipelines: Design, develop, and maintain scalable ingestion and
transformation pipelines using Microsoft Fabric (Data Factory in Fabric / Pipelines),
ADF/Synapse Pipelines, OneLake storage patterns, PySpark, Python, and SQL across
structured and unstructured data.
 API-driven and scheduled workflows: Develop pipelines that ingest data from external
APIs on a scheduled basis and initiate end-to-end downstream processing, supporting
one or multiple daily runs through to curated and consumption-ready layers.
 Data ingestion & integration: Integrate data from cloud and on-prem sources including
databases, third-party systems, files, and REST/SOAP APIs (auth, throttling, pagination,
retries, and error handling).
 Transformation & data modeling: Build curated layers and consumption-ready models;
implement incremental and batch processing logic; apply data modeling and
transformation best practices aligned to reporting/analytics needs.
 SQL development & tuning: Develop and optimize complex queries, stored procedures,
views, and datasets for efficient analytics and reporting; partner with analytics teams to
meet performance SLAs.
 Performance tuning & cost optimization: Tune Spark jobs, ADF data flows and SQL
workloads (partitioning, caching, parallelism, cluster sizing/configs) to improve reliability
and reduce runtime/cost.
 Business logic implementation: Translate requirements into scalable rules (validation,
eligibility, availability calculations), manage exceptions, audit logging, and ensure data
consistency across systems.
 Data quality & validation: Implement automated data quality checks, validation
frameworks, reconciliations, and monitoring to ensure trusted datasets.
 Security & compliance: Implement secure access via Azure AD, Managed Identities,
RBAC, least privilege, and secure connectivity to data lake, Fabric/Synapse, and APIs.
 Automation & CI/CD: Build deployment automation using Azure DevOps/Git, promoting
code across environments with consistent release practices; support testing and release
activities.
 Monitoring & troubleshooting: Monitor pipelines and jobs using Spark UI and Azure Log
Analytics; triage failures, perform root-cause analysis, and improve resiliency/runbooks.
 Collaboration: Work closely with architects, platform/DevOps engineers, analysts, and
data scientists; participate in design sessions and code reviews; operate within
Agile/Scrum delivery.

Tools & Technologies
 Fabric: Microsoft Fabric Workspaces, OneLake, Fabric Pipelines / Data Factory in Fabric,
Lakehouse/Warehouse (as applicable)
 Azure: ADLS Gen2, Blob Storage, Synapse Analytics, App Service (as needed), Azure
Databricks
 Languages: PySpark, Python, SQL (T-SQL)
 DevOps: Azure DevOps, Git, Terraform (preferred)
 Monitoring: Spark UI, Azure Log Analytics
 Data Governance: Azure purview
 AI Tools: Co-pilot, Claude.