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Open roles tracked
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Posted last 7 days
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Posted last 30 days
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What employees say
+ Work-Life Balance,Modern tech stack,Job stability,Learning culture
− Highly project oriented, Legacy Systems
Recent Glassdoor reviews · 12,049 total
Pros: Work-Life Balance,Modern tech stack,Job stability,Learning culture
Cons: Highly project oriented, Legacy Systems
View on GlassdoorPros: Lots of learning, great work life balance and a good place to work
Cons: Culture is changing and there is a new way of working now
View on GlassdoorPros: Good to work here no issues
Cons: I don't see any cons
View on GlassdoorPros: • Good work-life balance • Supportive and helpful team members • Learning opportunities and skill development • Friendly work environment • Salary and benefits are decent • Flexible working hours / hybrid work option • Good management support • Career growth opportunities • Exposure to new technologies and projects
Cons: “Work can be demanding at times, but it provides a good opportunity to learn and improve skills.”
View on GlassdoorPros: Overall good company. Hybrid work model, free cab facility
Cons: After management got changed, I felt BPO environment. I hpe atleast now this is changed.
View on GlassdoorCompany review data sourced from Glassdoor.
Glassdoor salary estimates for top hiring roles · 20,461 salary reports on Glassdoor
| Role | Location | Median | Range (LPA) | Reports |
|---|---|---|---|---|
| Sr. Data Engineer | India | — | — – — | — |
Salary benchmarks are estimates from Glassdoor and may differ from individual job listings.
India-scoped Glassdoor interview reports · 52 total · avg difficulty 2.9/5 · 51 questions indexed
consists of an initial HR phone screen, technical or analytical assessments, and 1-to-3 rounds of behavioral panel interviews. . The company heavily evaluates candidates against their "VACC" leadership model: Visionary, Architect, Catalyst, and Coach.
Sample questions
Thorough with a ppt round followed by questions regarding the disease and molecule. In the ppt, one has to prepare it on the basis of an article provided as well as google research. There may also be a few HR questions.
Sample questions
Online questions, Telephone interview , AC - included: presentation, group task + panel interview. 1st two stages were good but final stage was tough. Rejected via phone call which was worse than an email.
I participated in Bayer’s Data Engineer hackathon in December 2025 and unfortunately had a very disappointing experience with the assessment process. The hackathon is structured in two sprints: Sprint 1 (group activity): Understand the problem, design architecture & data model. The entire group is evaluated and presented together. Sprint 2 (individual): Actual implementation in Databricks – but only for candidates who clear Sprint 1. I was eliminated after Sprint 1. The biggest issue is that Sprint 1 performance is judged at group level, yet Sprint 2 (the actual coding) is individual. This means if you land in a weaker or less vocal group, you pay the price even if you personally contributed good ideas. Several participants (including me) felt we weren’t given a real chance to showcase individual strength because the evaluators (Chapter Lead + Architect) seemed to have very specific preconceived solutions in mind. Any deviation or alternative approach—even if valid—was dismissed. The evaluators rarely probed individual contributions during the presentation; they judged the final deck and the group’s presentation skills. People who were quiet or got overshadowed (common in newly formed mixed-experience teams) were automatically filtered out without ever writing a single line of code or demonstrating hands-on Databricks skills—the core requirement for a Data Engineer role. They also infront of our team crossed the entire squad which was unprofessional as we should never demean the candidates and be extremely polite with them. It felt more like a test of “read the interviewer’s mind” and “get lucky with strong teammates” than a fair evaluation of data engineering capability. Ironically, the implementation round that actually matters is individual, but many capable candidates never reach it because of this flawed group filter. Pros: Real-world problem statement, good exposure to Databricks environment for those who make it to Sprint 2. Cons: Heavily team-dependent elimination in round 1, rigid expectation of one “correct” architecture, almost no opportunity to demonstrate individual technical depth. I would strongly advise future candidates to be very vocal in the group and try to align exactly with what the evaluators hint at, because individual brilliance won’t save you if the group output doesn’t match their exact mental model. I came out feeling the process was unfair and not reflective of real data engineering skills. Hope Bayer reconsiders making the architecture round individual or at least evaluates individual contributions properly. Would not recommend applying if the role uses this hackathon format.
Sample questions
2 rounds of interview. One virtual, another face to face in the mumbai office. Well communicated in advance. Hr round was telephonic. Not difficult in nature. Easy qstn on Ind As were asked and about practical exposure
Sample questions
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