Manager, Data Engineering-IT

Job Description

Manager, Data Engineering-IT (341955)

341955

Textron Aviation has been inspiring the journey of flight for nine decades through the iconic and beloved Cessna and Beechcraft brands. We are passionate advocates of aviation, empowering people with the freedom of flight. As you join our legacy as a global leader in private aviation, you’ll have opportunities to try new fields, expand your skills and knowledge, stretch your abilities, and build your career.

We provide a competitive and extensive total rewards package that includes pay and innovative benefits to support you and your family members – now and in the future, beginning day one. Your success is our success.

The Manager – Data Engineering leads a team responsible for designing, building, and operating trusted, scalable data products that enable reporting, analytics, AI, and operational use cases. This role ensures that data pipelines, data models, and platform capabilities are delivered with strong engineering practices (automation, testing, observability, and security) and are aligned to business priorities. The manager promotes adoption of modern platforms such as Microsoft Fabric and Azure data services and fosters a collaborative environment that emphasizes technical excellence, continuous learning, and cross‑functional engagement.

The Manager – Data Engineering is an IT leadership role focused on building and continuously improving a mature data platform and reusable data products that power enterprise reporting and self‑service analytics, enable AI/ML solutions, and support operational decision‑making. This manager partners with IT and business stakeholders to define data product roadmaps, establish engineering standards, and ensure reliable deployment and performance across environments.

At Textron Aviation, we are building a community of Data & Analytics professionals with a shared commitment to excellence, stewardship, and strategic impact. This manager not only collaborates with peers across the organization but also plays a pivotal role in shaping analytics strategy, influencing enterprise decision‑making, and championing data governance. Through elevated stewardship, the manager ensures that analytics efforts are aligned with long‑term business goals and global standards.

• Lead the design, development, and deployment of data products (pipelines, curated datasets, and semantic layers) that enable scalable reporting and analytics.
• Partner with stakeholders to translate business needs into data product roadmaps, platform capabilities, and prioritized engineering backlogs.
• Establish and enforce engineering best practices for data ingestion, transformation, testing, version control, CI/CD, and release management.
• Manage and mentor data engineers; build team capability across data modeling, cloud services, automation, and operational support.
• Ensure data quality, documentation, and governance are embedded in delivery (metadata, lineage, access controls, and stewardship coordination).
• Operate and optimize the data platform to meet reliability, performance, and cost targets (monitoring, incident response, capacity planning, and cost management).

• Bachelor’s degree required in MIS, Computer Science, Data Analytics, Engineering, Mathematics, or related field.
• Minimum 7 years of relevant experience.
• Experience with SQL and Python, plus modern data engineering platforms/services (e.g., Microsoft Fabric, Azure Data Factory, Synapse, Databricks, or equivalent) required.
• Experience leading data engineering initiatives from concept to production, including stakeholder engagement, delivery planning, and mentoring engineers/analysts required.

• Strong written and verbal communication skills.
• Experience with Microsoft Office including Excel and PowerPoint.
• Practical application experience with data engineering tools and languages such as SQL, Python, Spark, and orchestration frameworks (e.g., Azure Data Factory or equivalent).
• Practical application experience with data architecture patterns and storage technologies (data lakes/lakehouse, warehouses, relational databases)…