Data Engineer (Azure) - 20576

• Design and build scalable data pipelines for both batch and streaming workloads.
• Develop Data Lake and Lakehouse solutions using Azure Data Factory, Azure Databricks, ADLS, PySpark, and Azure SQL Database.
• Build and integrate REST APIs to move and expose data across systems.

• Process, transform, validate, and harmonize structured, semi-structured, and unstructured data.
• Work fluently across Delta Lake, Parquet, Avro, JSON, and CSV formats.
• Implement data-quality controls, validation logic, and exception handling to keep data trustworthy.

• Schedule, monitor, troubleshoot, and optimize pipelines and Spark workloads.
• Build in logging, alerting, and observability so issues are caught before clients notice them.
• Support deployment and monitoring across development, testing, staging, and production environments.

• Support BI, analytics, and Data Science teams with secure, governed access to data.
• Collaborate directly with clients, architects, consultants, vendors, and development teams.
• Take ownership of deliverables and timelines, and document pipelines and data models so the team can scale.

• 3+ years in data engineering, big data, or cloud data solutions, including at least 3 years hands-on with Microsoft Azure.
• Strong hands-on experience with Azure Data Factory, Azure Databricks, ADLS, PySpark, Azure SQL Database, Python, and SQL.
• Practical experience building Data Lake or Lakehouse solutions and batch or streaming pipelines.
• Working knowledge of Spark and one or more of: Azure Event Hubs, Kafka, Airflow, or Hadoop.
• Solid grasp of data security, governance, access controls, data quality, source control, CI/CD, and automated deployments.
• Ability to write modular, scalable, maintainable, production-quality code with strong debugging skills.
• Strong written and spoken English for direct client communication. Native or professional proficiency in Spanish or Portuguese.
• Proven ability to work independently while collaborating effectively with U.S.-based clients and global teams.

• Hands-on experience with Databricks Unity Catalog and Medallion Architecture.
• Experience with real-time data processing, monitoring, and observability tooling.
• Experience working directly with client stakeholders in an Agile delivery environment.
• Relevant Microsoft Azure or Databricks data-engineering certification.

• A reliable, professional remote workspace with a suitable laptop and high-speed internet.
• Dependable availability with meaningful daily overlap with U.S. business hours.

You are:

• A hands-on owner — you take a deliverable across the finish line without needing to be managed to it.
• Client-ready — you can explain a technical trade-off clearly to a U.S. stakeholder, in English, without hand-holding.
• Rigorous — you care about data quality, governance, and code that survives production.
• Proactive — you surface risks early, ask the right questions, and don't wait to be told a pipeline is failing.
• Adaptable — comfortable in a distributed, fast-moving delivery environment with shifting client priorities.