Associate – AI & Data Engineering

Carrier’s interview process typically spans two to three rounds. The first round is an online aptitude test assessing logical, quantitative and verbal abilities. The technical round focuses on data engineering fundamentals—SQL queries, Python coding, ETL concepts, and basic big‑data tools. The final HR round evaluates cultural fit and communication skills. Preparation should include hands‑on practice with data pipelines, SQL optimization, and problem‑solving under time pressure.

Carrier promotes a performance‑driven yet supportive culture where learning and innovation are encouraged. Employees benefit from structured mentorship, regular skill‑upgradation workshops, and a healthy work‑life balance with flexible timings. The company’s global footprint offers exposure to cross‑regional projects and clear growth pathways.

Bachelor’s degree in Computer Science, Information Technology, Electronics & Communication, or related engineering discipline; minimum 60% aggregate or CGPA 6.0/10; graduating batch 2024‑2027; maximum of 2 backlogs allowed, which must be cleared before joining.

Round 1: Online Aptitude Test (logical reasoning, quantitative, verbal) → Round 2: Technical Interview (data engineering concepts, coding, SQL, system design) → Round 3: HR Interview (fitment, communication, career goals)

Carrier is a global leader in heating, ventilation, air‑conditioning and refrigeration solutions, with a strong presence in India for over three decades. The company prides itself on innovation, sustainability and delivering intelligent building solutions that improve comfort while reducing energy consumption. With a workforce of more than 10,000 in India, Carrier fosters a collaborative environment where engineers and data scientists work together to transform massive streams of sensor data into actionable insights for smart buildings and industrial applications. As an Associate – AI & Data Engineering, you will be part of the Digital Technology wing that drives Carrier’s data‑first strategy. You will collaborate with senior data engineers, AI researchers and product managers to design, build and maintain data pipelines that ingest, clean, and store data from IoT devices, building management systems and enterprise applications. Your work will directly enable predictive maintenance, energy‑efficiency analytics and AI‑powered control algorithms that are core to Carrier’s next‑generation product portfolio. **Key Responsibilities** 1. Design, develop, and maintain scalable ETL pipelines using Python, SQL and modern data‑flow tools. 2. Build and optimise data models in relational (PostgreSQL, MySQL) and big‑data platforms (Hive, Snowflake). 3. Implement data ingestion frameworks for IoT sensor streams using Kafka or MQTT. 4. Collaborate with AI teams to provide clean, feature‑engineered datasets for machine‑learning models. 5. Monitor data quality, troubleshoot pipeline failures, and ensure data governance compliance. 6. Write efficient Spark jobs for batch processing and real‑time analytics. 7. Participate in code reviews, adopt CI/CD practices, and maintain version control using Git. 8. Document data schemas, workflows and create dashboards for stakeholder visibility. 9. Stay updated with emerging cloud services (AWS, Azure, GCP) and recommend cost‑effective solutions. 10. Contribute to knowledge‑sharing sessions and mentor junior interns. **Tech Stack**: Python, SQL, Apache Spark, Hadoop, Kafka, Airflow, Docker, Kubernetes, AWS/GCP, Tableau/PowerBI, Git. **Growth Path**: Starting as an Associate, you can progress to Data Engineer, Senior Data Engineer, and eventually lead a Data Platform or AI Solutions team. Carrier encourages continuous learning through internal certification programs, sponsorship for external courses, and exposure to cross‑functional projects. **Why Join Carrier?** The role offers hands‑on experience with real‑world IoT data at scale, mentorship from industry veterans, and a chance to contribute to sustainable building technologies. Carrier’s inclusive culture, competitive compensation, and clear career progression make it an ideal launchpad for fresh engineering talent aspiring to become data‑driven innovators.