[Hiring] Data & Integrations Engineer @Mobile Communications America

Role Description

MCA is seeking a Data & Integrations Engineer to serve as the technical bridge between its Data Warehouse and Business Systems teams, and to lead the organization's adoption of AI-assisted development practices. This is an internal position. The Data & Integrations Engineer will own the management and design of MCA's Microsoft Azure data warehouse architecture, including its ETL processes, semantic layer and reporting infrastructure. The role will also partner closely with the Business Systems team to design, implement, monitor and improve integrations among the company's various operational and backend systems and the data warehouse.

The successful candidate should be proficient in using AI tools to deliver results and conduct testing, including validating AI-generated work and continually exploring and sharing effective practices across working teams.

Essential Duties and Responsibilities

• Azure Data Warehouse
  • Maintain and enhance ETL processes that ingest data from multiple internal sources into the Azure data warehouse.
  • Own MCA's ETL pipeline, directing and supporting external consulting partners on its updates and ongoing management.
  • Proactively identify risks and opportunities for improvement in the ETL pipeline, strengthening its redundancy and consistency.
  • Design and implement new schemas, pipelines and semantic models to support new data sources and reporting requirements.
  • Collaborate with reporting and data analysts to ensure the accuracy, performance and usability of the reporting layer.
  • Troubleshoot, debug and optimize queries, stored procedures and pipeline jobs.
  • Implement and maintain security, access and data protection controls across warehouse objects.
• Integrations and Business Systems
  • Partner with the Business Systems team to design, build, monitor and overhaul integrations among NetSuite, HubSpot, other business systems and the data warehouse.
  • Develop and maintain integrations using Celigo, the company's primary integration platform, and evaluate alternative platforms where appropriate.
  • Establish monitoring and alerting for integrations, and resolve failed synchronizations and data discrepancies.
  • Evaluate and onboard new systems and data sources, including APIs and webhooks.
  • Assist with the configuration and extension of business systems where data or automation changes are required.
  • Coordinate between the Data Warehouse and Business Systems teams to ensure that changes in one environment do not adversely affect the other.
• Internal Azure Applications
  • Own and maintain internally developed Azure-based applications, ensuring their reliability, security and performance.
  • Partner with business stakeholders to design and deliver new features in response to business requests.
  • Identify, diagnose and resolve application defects in a timely manner.
  • Perform quality assurance testing on new features and fixes prior to release.
• Internal Tools and Automation
  • Gather requirements from business stakeholders and deliver tools, reports, workflows and applications that address them.
  • Identify and automate manual, repetitive processes across finance, sales and operations.
  • Support, consolidate or retire legacy tools developed when this function served external customers.
• AI-Assisted Development
  • Use AI tools where appropriate to support coding, code review, testing and documentation, while applying strong hands-on development skills and independent technical judgment.
  • Stay informed about emerging AI tools and techniques, evaluate their practical value and share relevant findings with both teams.
  • Identify opportunities to create reusable prompts, utilities or connectors that complement established development practices and improve team productivity.
  • Follow and contribute to standards for the safe and appropriate use of AI with company data and systems.
• Documentation and Quality Assurance
  • Produce and maintain technical documentation, runbooks and data definitions.
  • Test all changes prior to release and adhere to established deployment procedures.

Qualifications

• Five or more years of experience in data engineering, business intelligence development, systems integration or a related technical role.
• Strong proficiency in SQL and experience with relational databases, preferably Azure SQL Database or Microsoft SQL Server.
• Demonstrated experience building and supporting ETL or ELT pipelines for a data warehouse.
• Working knowledge of Microsoft Azure data services, such as Azure SQL, Azure Data Factory, Azure Functions, Logic Apps and Azure Storage.
• Experience with REST APIs, webhooks and system-to-system integrations.
• Regular, hands-on use of AI development tools (for example, Claude, Claude Code or GitHub Copilot) in production work.
• Sufficient technical fluency to review, test and correct AI-generated code.
• Experience writing and maintaining unit tests for application, database and integration code.
• Experience with Azure DevOps or GitHub for source control, build pipelines and release management.
• Excellent written and verbal communication skills with both technical and business audiences.
• Ability to work independently while maintaining close coordination with multiple teams.
• Travel as necessary to support company and customer needs.

Preferred Qualifications

• Experience with Celigo or a comparable integration platform, such as Boomi, Workato or Azure Logic Apps.
• Experience with NetSuite (SuiteQL, saved searches, SuiteScript or SuiteTalk) and HubSpot (APIs, workflows and data model).
• Experience developing reports or data sources for Tableau or another business intelligence tool.
• Experience building AI agents, MCP connectors, Claude skills or shared prompt libraries.
• Proficiency in C#/.NET (including Razor Pages), JavaScript or Python, and experience with Azure App Services and Visual Studio database projects.
• Familiarity with Agile methodologies.
• Background in financial or operational reporting.

Core Competencies

• Intellectual curiosity regarding AI: Actively explores new AI tools as they emerge, tests their limits, and can articulate where they add value and where they fall short.
• Direction and ownership: Approaches AI as a resource to be directed, providing clear objectives and context, validating outputs and taking full accountability for results.
• Cross-functional collaboration: Works effectively across teams and investigates how changes in one system affect others.
• Commitment to quality: Balances speed with thorough testing, documentation and protection of company data.
• Knowledge sharing: Shares lessons learned and helps colleagues on both teams adopt AI tools effectively.