DescriptionJob overview and responsibilities The Data Analytics Manager leads the development and delivery of data products, analytics solutions, and AI/ML-enabled capabilities that drive measurable business outcomes. This role partners with business, product, and technology stakeholders to identify opportunities, define requirements, and deliver scalable solutions that improve operational performance, decision-making, and organizational effectiveness.
- Understands how data, analytics, and technology capabilities support business and operational objectives
- Translates complex business challenges into actionable analytics, automation, and AI/ML-driven solutions
- Leads the development of scalable data products and analytics workstreams that deliver measurable value across the enterprise
Areas of focus will include, but are not limited to:- Data Products & Analytics Solutions:
- Develop and deliver data products, self-service capabilities, and analytics solutions that drive measurable business outcomes
- Partner with stakeholders to define requirements, KPIs, and success measures
- Ensure the quality, integrity, and governance of enterprise data used for analytics and decision-making
- AI/ML & Automation:
- Identify opportunities to leverage AI/ML, automation, and advanced analytics to improve business processes and operational performance
- Evaluate and adopt emerging technologies that create measurable value
- Stakeholder Leadership:
- Partner with business, product, technology, and operational teams to align priorities and deliver solutions
- Translate complex technical concepts and analytical findings into actionable recommendations
- Lead cross-functional initiatives that drive organizational outcomes
- Technical Leadership:
- Lead the design and delivery of scalable analytics, AI/ML, and data solutions across enterprise platforms
- Mentor analysts and technical professionals while promoting data-driven decision-making and continuous improvement
Qualifications What's needed to succeed (Minimum Qualifications): - Bachelor's Degree
- Mathematics, Computer Science, Information Management, Statistics
- At least 4 years of experience in roles like Data Analyst or Business Intelligence Developer
- Strong data integration skills across siloed systems and data sources
- Expertise in BI dashboard creation using tools such as Power BI and Spotfire to deliver insightful and visually compelling dashboards
- Advanced proficiency in SQL and data modeling
- Ability to gather, clarify, and translate business requirements into actionable data and analytics solutions
- Demonstrated ability to use data and analytical methods to solve real-world business problems
- Experience leveraging AI, machine learning, and automation technologies to improve analytics, operational efficiency, and business decision-making
- Ability to communicate complex technical concepts and analytical findings to both technical and non-technical audiences
- Must be legally authorized to work in the United States for any employer without sponsorship
- Successful completion of interview required to meet job qualification
- Reliable, punctual attendance is an essential function of the position
What will help you propel from the pack (Preferred Qualifications): - Master's degree
- Background in the airline industry
- Proficiency in developing self-service dashboards with Power BI and Spotfire
- Relevant certifications such as AWS Certified AI Practitioner (AIF-C01), AWS Certified Cloud Practitioner (CLF-C02), or equivalent AI and cloud certifications
- Experience with AWS services like Redshift, Athena, Aurora
- Exposure to programming and data processing languages like Python, Spark, R
- Experience leveraging generative AI, machine learning, or intelligent automation technologies to improve business processes and decision-making
- Experience with enterprise analytics platforms, cloud data technologies, and AI-enabled data solutions
- Experience developing data products, self-service capabilities, and analytics solutions that drive measurable business outcomes
- Experience working with large-scale operational, customer, or real-time data environments
