Data Engineer

Cisco’s interview process typically starts with an online coding test focusing on SQL queries and Python data‑manipulation tasks. This is followed by two to three technical rounds that dive deep into data‑pipeline design, Snowflake/DBT expertise, and AI‑tool integration, ending with an HR conversation about cultural fit and career goals. The difficulty is moderate to high, so strong fundamentals and hands‑on project experience are essential.

Cisco promotes a collaborative, inclusive environment where engineers are encouraged to experiment and innovate. Employees benefit from structured career growth programs, mentorship, and a healthy work‑life balance supported by flexible hybrid policies.

Graduates (B.Tech/B.E/M.Tech/M.Sc) in Computer Science, Information Technology, Electronics, or related streams; minimum 60% aggregate (or CGPA 6.5/10); no active backlogs at the time of joining; graduation batch 2025‑2027 (freshers); strong foundation in data structures, algorithms, databases, and cloud platforms.

Round 1: Online coding assessment (SQL & Python problems) → Round 2: Technical interview (data engineering concepts, Snowflake, dbt, pipeline design) → Round 3: System design & AI‑tooling discussion → Round 4: HR interview (cultural fit, compensation, career aspirations)

Cisco is a global technology leader that designs, manufactures, and sells networking hardware, software, telecommunications equipment and other high‑technology services and products. With more than 40 years of innovation, Cisco has built a reputation for delivering secure, reliable, and scalable solutions that power enterprises, service providers, and governments worldwide. In India, Cisco operates large development centers, research labs, and sales offices, offering a vibrant ecosystem for engineers to work on cutting‑edge projects that shape the future of digital transformation and AI‑driven analytics. The Growth Metrics Data Domain team sits within Cisco’s Data and Analytics organization and is responsible for the data infrastructure that fuels the company’s revenue‑growth engine. This AI‑native team builds and maintains the pipelines, data models, and analytical frameworks that enable real‑time insights for renewals, sales, finance, and executive reporting. Engineers in this group enjoy high visibility, direct impact on strategic decisions, and the opportunity to experiment with emerging AI tools such as Snowflake Cortex and dbt Copilot. As a Data Engineer on this team, you will design, develop, and operate enterprise‑scale data pipelines that ingest, transform, and store massive volumes of structured and semi‑structured data. You will work closely with data scientists, product managers, and business stakeholders to ensure that data is accurate, timely, and ready for advanced analytics and AI‑augmented insights. The role demands strong ownership, a deep understanding of cloud data warehousing, and a forward‑looking mindset toward AI‑enabled data tooling. Key Responsibilities: 1. Design, build, and maintain scalable data pipelines on Snowflake to ingest data from diverse sources. 2. Develop robust data models and dimensional schemas that support reporting, BI, and machine‑learning workloads. 3. Write production‑grade SQL and Python scripts for data transformation, validation, and automation. 4. Implement data quality frameworks using dbt tests, row‑count checks, null assertions, and referential integrity validations. 5. Collaborate with AI teams to create pipeline structures that ingest LLM‑generated insights and support AI‑driven analytics. 6. Evaluate and adopt AI‑native tooling such as Snowflake Cortex, dbt Copilot, and related services. 7. Optimize query performance, manage RBAC, and ensure security compliance in Snowflake environments. 8. Participate in code reviews, CI/CD pipelines, and Git‑based version control for all data assets. 9. Monitor pipeline health using orchestration tools like Airflow or dbt Cloud and set up alerts for failures. 10. Mentor junior engineers and contribute to documentation, best‑practice guides, and knowledge‑sharing sessions. Tech Stack: Snowflake, dbt, SQL, Python (pandas, numpy), Airflow/Prefect, Git, CI/CD, Snowflake Cortex, LLM APIs, Power BI/Business Objects for downstream consumption. Growth Path: Starting as a Data Engineer, you can progress to Senior Data Engineer, Data Architecture Lead, or AI‑Enabled Analytics Manager, with opportunities to lead cross‑functional projects and influence Cisco’s global data strategy. Why Join Cisco? You will be part of a world‑renowned technology brand that invests heavily in innovation, employee development, and inclusive culture. Cisco offers competitive compensation, continuous learning programs, and the chance to work on data products that impact millions of customers worldwide. The hybrid work model provides flexibility while keeping you connected to a high‑performing team.