Advanced Data Analyst

Candidates typically face a four‑stage process: an online aptitude test, a technical interview focusing on SQL, Power BI, and data modeling, a case‑study presentation where they analyze a dataset and propose insights, and finally an HR interview assessing cultural fit and communication skills. The difficulty is moderate to high, with emphasis on practical problem‑solving and clear articulation of analytical findings.

Round 1: Online Aptitude & Logical Reasoning Test → Round 2: Technical Interview (SQL, Power BI, Data Modeling) → Round 3: Case Study / Business Analytics Presentation → Round 4: HR Interview

CAES by Honeywell fosters a collaborative, innovation‑driven culture where employees are encouraged to experiment with new technologies and share knowledge. The organization emphasizes work‑life balance, offers flexible hybrid schedules, and provides clear career progression pathways with regular learning and upskilling opportunities.

Bachelor's or Master's degree in Data Analytics, Computer Science, Information Technology, Electronics & Communication, or related engineering discipline; minimum 60% aggregate (or CGPA 6.5/10); no active backlogs at the time of joining; final year students eligible for pre‑placement; strong analytical mindset and proficiency in SQL and Power BI required.

CAES by Honeywell is a strategic business unit of Honeywell International that delivers cutting‑edge digital solutions across aerospace, building automation, energy, and industrial automation. The unit leverages the Honeywell Forge platform to turn data into actionable insights, helping customers improve safety, efficiency, and sustainability. With a strong focus on innovation, CAES invests heavily in research, advanced analytics, and AI‑driven automation, creating a vibrant ecosystem where technology and business intersect. Employees benefit from a global brand reputation, exposure to industry‑leading projects, and a culture that encourages continuous learning and cross‑functional collaboration. The role of Advanced Data Analyst is positioned at the heart of this data‑centric organization. The analyst will partner with business units, engineering teams, and senior leadership to translate complex data sets into clear, actionable business intelligence. Responsibilities span the full analytics lifecycle: from data ingestion, cleansing, and transformation, through model development and validation, to the creation of interactive visualizations and dashboards that drive strategic decisions. The position also involves leveraging generative AI tools to accelerate solution delivery, ensuring that analytics outputs are not only accurate but also scalable and maintainable. Key responsibilities include: 1. Designing, developing, and maintaining Power BI dashboards and self‑service reports that surface critical performance metrics. 2. Writing optimized SQL queries and building robust data models to support ad‑hoc analysis and recurring reporting. 3. Applying AI prompting techniques to automate repetitive data‑preparation tasks and enhance analytical productivity. 4. Conducting data quality assessments, implementing validation rules, and establishing governance frameworks to guarantee data integrity. 5. Collaborating with stakeholders to gather requirements, translate business needs into technical specifications, and ensure alignment throughout the project lifecycle. 6. Performing advanced statistical analysis and predictive modeling to uncover trends, forecast outcomes, and recommend actionable insights. 7. Identifying process bottlenecks in the analytics workflow and proposing process‑improvement initiatives that increase efficiency. 8. Supporting change‑management activities, including user training, documentation, and post‑deployment monitoring to drive adoption of new analytics solutions. 9. Staying abreast of emerging technologies in data engineering, visualization, and AI, and evaluating their applicability to CAES’s analytics stack. 10. Contributing to a culture of knowledge sharing by mentoring junior analysts and documenting best practices. The technical stack for this role includes Power BI for visualization, Microsoft SQL Server (or equivalent) for data warehousing, advanced SQL for data manipulation, and familiarity with AI‑assisted analytics platforms (e.g., Azure OpenAI, Copilot). A solid foundation in data modeling, ETL processes, and statistical analysis is essential. The role offers a clear growth trajectory: high‑performing analysts can progress to Senior Data Analyst, Data Scientist, or Analytics Lead positions, with opportunities to lead cross‑functional projects and influence enterprise‑wide data strategy. Joining CAES by Honeywell means becoming part of a forward‑thinking organization that values innovation, offers competitive compensation, and provides a supportive environment for fresh talent to thrive and make a tangible impact on global industries.