Data Engineer (Azure)

Location: Remote Employment Type: Full-Time IC Experience: 3+ Years Work Hours: Aligned to U.S. Business Hours

About the Company

We are a rapidly growing U.S.-based data and analytics services firm that designs and delivers modern cloud data platforms for our clients. As our delivery work scales, we are building a dedicated Latin America engineering team that partners directly with U.S. stakeholders — not behind a layer of project managers, but as hands-on owners of the data solutions our clients rely on.

You would be joining a distributed team where autonomy is expected, delivery is visible, and the engineering standard is high.

About the Role

This role exists because our Azure data practice is growing faster than our current delivery capacity. We need an engineer who can own the pipeline — literally and figuratively — from design through production monitoring, while working shoulder-to-shoulder with U.S. clients, architects, and analytics teams.

You will not be handed narrow tickets. You will be trusted to build scalable, governed, production-grade data solutions on Azure and Databricks, communicate directly with client stakeholders, and take responsibility for the outcome. The best fit for this role is someone who is equally comfortable writing clean PySpark and explaining a design decision to a client on a call.

Key Responsibilities

Pipeline & Platform Development

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.

Data Processing & Quality

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.

Operations & Reliability

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.

Collaboration & Delivery Ownership

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.

Required Qualifications

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.

Preferred Qualifications

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.

Ideal Candidate Profile

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.

Remote Work Requirements

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

Data Engineer (Azure) - 20576