Job Description
**Data Systems Architect** | Gurugram | Hybrid | INR 38.6-76.1 LPA
**About the role:**
- Position within Data Engineering, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for Data Engineering
**What you'll do:**
- Handle core data engineering activities on a day-to-day basis
- Use Deep Learning regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Deep Learning
- TensorFlow/PyTorch
- Data Preprocessing
- Data Engineering
- Statistical Modelling
- Computer Vision/NLP Basics
- Model Evaluation & Tuning
- Python
- M.Sc (Data Science)
- 13+ years of relevant experience
**Good to know:**
- In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
- Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. 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.
- High performers in this role typically move into a broader ownership scope within 18-24 months. The next logical step from this position is a senior or lead-track role within the same function. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- References may be requested closer to the offer stage rather than at the start of the process. Learning and certification support is available for tools and frameworks directly relevant to the role.
- The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
- The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Compensation includes standard statutory benefits along with performance-linked variable pay.
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
- Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.
**About the role:**
- Position within Data Engineering, under the Machine Learning vertical
- Level: senior level
- Reports to the functional head for Data Engineering
**What you'll do:**
- Handle core data engineering activities on a day-to-day basis
- Use Deep Learning regularly as part of core deliverables
- Work closely with adjacent teams to align priorities
- Track progress against agreed timelines and escalate issues proactively
**What you'll need:**
- Deep Learning
- TensorFlow/PyTorch
- Data Preprocessing
- Data Engineering
- Statistical Modelling
- Computer Vision/NLP Basics
- Model Evaluation & Tuning
- Python
- M.Sc (Data Science)
- 13+ years of relevant experience
**Good to know:**
- In addition to fixed pay, the role carries eligibility for annual appraisals and merit-based increments. Performance is reviewed on a structured cycle, with clear criteria for movement into the next band.
- Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability. Managers here tend to give context rather than instructions, leaving room for the individual to decide how a task gets done.
- Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth. The role alternates between independent deep work and short coordination huddles with stakeholders across functions. Candidates are encouraged to highlight measurable outcomes from previous roles rather than just responsibilities held.
- New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. 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.
- High performers in this role typically move into a broader ownership scope within 18-24 months. The next logical step from this position is a senior or lead-track role within the same function. No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions.
- References may be requested closer to the offer stage rather than at the start of the process. Learning and certification support is available for tools and frameworks directly relevant to the role.
- The hiring process typically includes a screening round, a technical or functional assessment, and a final discussion with the reporting manager. The team follows a flat structure where ideas are evaluated on merit rather than hierarchy, and feedback flows in both directions. Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round.
- The organisation places a premium on ownership; once a task is assigned, the expectation is to see it through without hand-holding. There is a defined internal mobility path for people who want to specialise deeper or move into people management. Compensation includes standard statutory benefits along with performance-linked variable pay.
- The environment rewards initiative - people who flag problems early and propose fixes tend to grow faster here. Most days include at least one review checkpoint where progress is discussed against the plan for the week.
- Skip-level conversations with senior leadership are encouraged at least twice a year to discuss career direction. Work culture here leans collaborative, with regular knowledge-sharing sessions and an open-door policy for raising concerns. Leave policy follows standard industry norms, including earned leave, sick leave, and public holidays as applicable.