Senior Database Operations Engineer
Job description
About the role
The position drives 100 percent automation and deep monitoring for data services. This role partners with data and software engineers to optimize application performance and reliability.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
What you'll do
Database strategy is shaped through subject-matter expertise in engine best practices and long-term data placement. Teamwide database expertise rises as technical leadership partners with engineering leadership.
SQL performance analysis uncovers query execution bottlenecks.
Database internals are analyzed through deep-dive performance forensics.
Interface layers for database interaction are optimized, and organizational standards for indexing and schema configurations are established.
Requirements
A Bachelor's degree in Computer Science, Information Technology, or a related field is required.
Eight or more years of experience in database operations or a related role focused on data platforms and data stores is required.
Hands-on experience with AWS RDS, Aurora, and NoSQL services is mandatory.
Extensive experience with the AWS cloud platform and related data services is required.
Proficiency in monitoring tools such as Datadog, CloudWatch, DevOps Guru, and DB Performance Insights is required.
Programming skills in one or more languages such as Python or Java are required.
Understanding of performance metrics at both high and low levels, including disk or IO saturation, is required.
System bottlenecks are identified and eliminated as a core responsibility.
Database internals knowledge, including index types, schemas, and query plans, is required.
Experience with SQL and NoSQL systems and managing large-scale data infrastructures is required.
Basic implementation of CI/CD pipelines and DatabaseOps practices is required.
Data governance, compliance, and lifecycle management are handled as standard duties.
Independent ownership and execution of projects are balanced with team collaboration to shape data engineering vision.
Strong communication skills support mentoring and guidance for junior engineers.
Practical notes
This role requires onsite work in Hyderabad on a hybrid schedule. Candidates must hold eligibility to work in India without sponsorship. Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
Database operations teams typically rely on infrastructure as code to enforce consistent configurations. Monitoring platforms provide continuous insight into system health and performance. Automation reduces manual intervention and standardizes routine database tasks. Deep SQL and NoSQL knowledge helps maintain scalable and reliable data platforms. Strong problem-solving skills are essential for resolving complex performance issues.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.