Associate Engineer – Data Analytics

RTX’s interview process typically starts with a timed online aptitude test, followed by a technical interview focusing on Python, SQL queries, and data‑analysis scenarios. The HR round assesses cultural fit, communication skills, and career goals. Candidates should practice coding problems, brush up on data‑visualisation concepts, and be ready to discuss real‑world analytics projects.

RTX promotes a collaborative and inclusive work environment where engineers are encouraged to experiment and innovate. Employees benefit from structured learning programs, mentorship, and a balanced approach to work‑life integration, with flexible work arrangements where applicable.

Graduates of 2024, 2025 or 2026 batches with a B.Tech/B.E/M.Tech in Computer Science, Information Technology, Electronics & Communication, Electrical, or related engineering streams. Minimum aggregate of 60% (or CGPA 6.0/10) in the final degree. No active backlogs at the time of joining. Candidates must have a strong foundation in programming, statistics, and database concepts.

Round 1: Online Aptitude Test (Logical Reasoning, Numerical Ability, Verbal) → Round 2: Technical Interview (Python, SQL, Data Analytics concepts, problem‑solving) → Round 3: HR Interview (fitment, communication, career aspirations)

RTX (Raytheon Technologies) is a leading global aerospace and defense technology company with a strong presence in India. The firm designs, manufactures and services advanced systems for aviation, space, and security sectors. With a focus on innovation, RTX invests heavily in data‑driven engineering, enabling customers to make faster, more accurate decisions. The Indian operations serve as a hub for research, development, and analytics, offering fresh talent exposure to world‑class projects and cross‑functional collaboration across continents. The role of Associate Engineer – Data Analytics is crafted for recent graduates who are eager to apply their analytical mindset to real‑world engineering challenges. As a data analyst within RTX, you will work closely with senior engineers, product managers, and business stakeholders to transform raw data into actionable insights. Your work will directly influence design choices, operational efficiency, and strategic planning across the organization. **Key Responsibilities** 1. Collect, clean, and preprocess large datasets from engineering tools, sensors, and enterprise systems. 2. Perform exploratory data analysis to uncover trends, anomalies, and performance bottlenecks. 3. Develop interactive dashboards and visualizations using tools such as Power BI or Tableau to communicate findings. 4. Write efficient SQL queries and Python scripts for data extraction, transformation, and loading (ETL). 5. Collaborate with cross‑functional teams to define data requirements and deliver analytical solutions. 6. Build predictive models or statistical reports to support engineering decision‑making. 7. Document data pipelines, methodologies, and results for reproducibility. 8. Participate in code reviews and adopt best practices for version control (Git). 9. Stay updated with emerging analytics technologies and propose improvements to existing workflows. 10. Assist in preparing technical presentations for senior leadership and external partners. **Tech Stack**: Python (pandas, NumPy, scikit‑learn), SQL (MySQL/PostgreSQL), data‑visualisation tools (Power BI, Tableau), Git, Linux environment, basic knowledge of cloud platforms (AWS/Azure) is a plus. **Growth Path**: Starting as an Associate Engineer, high performers can progress to Senior Data Analyst, Data Engineer, or Analytics Lead within 2‑4 years. RTX encourages continuous learning through internal training programs, certifications, and opportunities to work on international projects. **Why Join RTX?** RTX offers a unique blend of cutting‑edge aerospace projects and a data‑centric culture. Freshers gain exposure to large‑scale engineering data, mentorship from industry veterans, and a clear career trajectory. The company’s commitment to innovation, diversity, and work‑life balance makes it an attractive launchpad for a long‑term career in data analytics and engineering.