Job Description
Data Operations Engineer | Jaipur | INR 10.3-20.9 LPA
Data Engineering role, mid level, full-time.
Need: M.Tech (Machine Learning), 3+ yrs.
Core skills: TensorFlow/PyTorch, Python, Feature Engineering, Statistical Modelling, Data Engineering.
If this fits your profile in data engineering, apply now.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
The next logical step from this position is a senior or lead-track role within the same function. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Interested candidates should be prepared to walk through relevant past work during the interview process.
The package is structured with a fixed component, performance bonus, and standard health coverage. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Learning and certification support is available for tools and frameworks directly relevant to the role. References may be requested closer to the offer stage rather than at the start of the process.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Compensation includes standard statutory benefits along with performance-linked variable pay. High performers in this role typically move into a broader ownership scope within 18-24 months.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
The organisation has historically promoted from within for a majority of its senior and lead openings. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving.
Data Engineering role, mid level, full-time.
Need: M.Tech (Machine Learning), 3+ yrs.
Core skills: TensorFlow/PyTorch, Python, Feature Engineering, Statistical Modelling, Data Engineering.
If this fits your profile in data engineering, apply now.
In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone.
The next logical step from this position is a senior or lead-track role within the same function. Relocation assistance may be extended for candidates moving from a different city, subject to approval. Interested candidates should be prepared to walk through relevant past work during the interview process.
The package is structured with a fixed component, performance bonus, and standard health coverage. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work.
Benefits include health insurance, provident fund contributions, and paid leave in line with company policy. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done. The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager.
Learning and certification support is available for tools and frameworks directly relevant to the role. References may be requested closer to the offer stage rather than at the start of the process.
The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Most days include at least one review checkpoint where progress is discussed against the plan for the week. Compensation includes standard statutory benefits along with performance-linked variable pay. High performers in this role typically move into a broader ownership scope within 18-24 months.
There is a defined internal mobility path for people who want to specialise deeper or move into people management. The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding.
The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns.
Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables.
Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership.
The organisation has historically promoted from within for a majority of its senior and lead openings. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving.