Job Description
Data Engineering Automation Engineer | Gurugram | INR 9.5-22.2 LPA
Data Engineering role, mid level, full-time.
Need: M.Sc (Data Science), 3+ yrs.
Core skills: Machine Learning Algorithms, MLOps, Statistical Modelling, Python, Deep Learning.
If this fits your profile in data engineering, apply now.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
The next logical step from this position is a senior or lead-track role within the same function. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Internal job postings are open to employees who've completed a minimum tenure in their current role. References may be requested closer to the offer stage rather than at the start of the process.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
Data Engineering role, mid level, full-time.
Need: M.Sc (Data Science), 3+ yrs.
Core skills: Machine Learning Algorithms, MLOps, Statistical Modelling, Python, Deep Learning.
If this fits your profile in data engineering, apply now.
Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
Disagreement on approach is expected and welcomed, provided it's backed by reasoning rather than preference alone. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band. Relocation assistance may be extended for candidates moving from a different city, subject to approval.
There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
The next logical step from this position is a senior or lead-track role within the same function. There is a defined internal mobility path for people who want to specialise deeper or move into people management.
Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Expect a mix of scheduled reviews, ad-hoc troubleshooting, and documentation as the workload shifts through the week.
Internal job postings are open to employees who've completed a minimum tenure in their current role. References may be requested closer to the offer stage rather than at the start of the process.
Cross-functional exposure is built into the role by design, so the person will regularly interact with adjacent teams and leadership. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.