Netomi Data Engineer I: remote India role requiring at least two years

About the Data Engineer I opening

Netomi lists this as a full-time, remote India engineering role. Its description also discusses collaboration with an analytics team associated with Gurugram. The position combines pipeline engineering with analysis that supports product operations and customer experience. Despite the level I title, the employer requests at least two years of hands-on data-engineering or backend-development experience.

Data pipelines, ingestion and reliable delivery

The engineering work includes building scalable pipelines and ETL or ELT processes that bring structured and unstructured information into usable systems. The notice names modern platforms such as Spark, Databricks, Airflow and Snowflake as examples. Reliability matters across ingestion and transformation, and Python code is expected to include unit and integration testing rather than only one-off analysis notebooks.

Analysis and collaboration with product teams

Exploratory analysis is part of the remit: checking integrity, finding trends and developing information that supports product decisions. Data scientists, analysts and software engineers collaborate on models and analytics. The description also mentions benchmarking product-operations projects and publishing scorecards. Applicants should therefore consider whether they enjoy both building the data system and explaining what its output means.

Education and professional experience

The requested qualification is a bachelor’s or master’s degree in computer science, engineering or a related subject. At least two years in data engineering or backend software development is specified. A recent graduate without that experience should not assume the role is open to freshers because of its title. Relevant work should be described in terms of the pipelines, services or production features actually delivered.

Core database and platform requirements

Requirements include Python, SQL, relational databases such as MySQL or PostgreSQL, and at least one orchestration tool such as Airflow, Luigi or Prefect. Experience with Kafka or RabbitMQ, cloud data platforms, modelling, warehousing and distributed systems is also requested. The named cloud-platform examples do not mean candidates must claim hands-on expertise in every provider listed.

Additional skills and published employment terms

DevOps practices, containers, data-security and privacy awareness, and exposure to AI-integrated products are additional skills. Prompt-engineering familiarity is also mentioned. These do not replace the core engineering requirements. A numerical salary, closing date and complete working-hours arrangement are not published. Confirm the reporting team and remote-work expectations during recruitment, particularly because the description also references Gurugram.

How to apply through Netomi

Use the employer’s Lever form for requisition 02b45afd-5a49-41b2-a244-b0ca76306692. Check that your résumé distinguishes professional experience from learning projects and accurately identifies your role in shared systems. The application page governs current availability. The advert does not set a public assessment pattern, number of interview rounds or guaranteed response period.

Practical preparation: show data reliability

One strong example can explain a source dataset, the pipeline you built, how you validated records and what happened when a job failed. Discuss late data, duplicates or retries if those were real problems in your work. Explain tests and monitoring as well as the output. This is role-specific preparation guidance, not an employer-issued take-home task or a list of mandatory interview topics.

Useful questions for the recruiting conversation

Ask about data volume, batch versus streaming work, the main orchestration platform and ownership of failures after deployment. Clarify how analytics and engineering responsibilities are divided and how product quality is measured. These questions follow the published mix of pipeline work and analysis and can help you judge the role without assuming it is solely a dashboard-building position.

Relevant skills

• Python
• SQL
• Data pipelines
• Apache Airflow
• Cloud data platforms
• Kafka