Engineering Manager

2 months ago

Bengaluru, India Talent Monitor Full time

Responsibilities :

- Hiring & retention - Have the right team structure, hire top notch talent in your team and create a culture that retains them. Keep team members motivated, ensure individual goal alignment with projects assigned.

- Culture - create an open & inclusive culture, culture that drives accountability in team members, improve collaboration within and across teams, culture that enables them to perform better, culture that enables experimentation, innovation and risk taking, culture of learning and development.

- Individual and career development - Invest deeply in mentoring, planning their career progression, meaningful 1:1s, additional mentorship and skill development plans, developmental feedback, performance appraisals.

- Product & Tech

- Influence tech & product roadmaps, key investments to make, key bets to take.

- Provide technical guidance as necessary in absence of senior lead / architect

- Ensure balance between business/product goals, tech debt reduction, long term platform and deep test investments.

- Identify opportunities for platformization, reduce tech fragmentation, reduce/remove duplicate effort within/across teams, etc.

- Planning & Prioritisation - Create 12+ month roadmap for product and tech investments, create execution roadmap, delivery milestones and schedule, drive alignment with all stakeholders and team members. Be outstanding with prioritisation and scoping.

- Execution - lead execution of the goals, take complete ownership of the quality & timeliness of deliverables, remove blockers, manage dependencies, resolve conflicts, provide timely visibility of execution/risks/slippages/etc, mitigate risks, etc.

- Issues & Quality - Accountable for quality of launches, ensuring products/features released meet all functional and nonfunctional (scale, availability, debuggability, security, etc) requirements, drive issue triaging and timely resolution.

- Processes & practices - Drive processes and best practices within the team for SDLC (code reviews, build & deployments, testing, design/arch reviews, test processes including coverage, documentation, etc), drive usage of right/better tools, remove manual effort, etc - continuously improve efficiency and reliability of teams and systems.

- Metrics - Keep laser focus on business, application and system metrics. Drive culture of being metric driven, define/refine metrics as appropriate, review metrics with team and stakeholders, take targets on metrics, etc

- Create self sufficient teams, create the next set of leaders to help scale yourself.

- Create plans for smooth onboarding of new team members.

Required :

- Bachelors (4 years) or higher in computer science or equivalent with 10+ years of software development experience

- 2+ years of people management experience, leading a team of 10-12 software engineers

- Successful track record of releasing highly scalable distributed systems (user flow, offline flow), evolving them, iteratively improving them.

- Excellent at mentoring/coaching and developing people.

- Ability to handle multiple teams, multiple projects, manage competing priorities in a fast paced startup environment.

- Prior experience in turning around low performers, growing senior talent.

- Strong experience in managing software development through Agile development processes

- Strong experience in SDLC processes and various tools and their tradeoffs

- Good communication, stakeholder management and collaboration skills.

- Ability to drive consensus and rally team members and stakeholders towards a shared vision/goal.

- Is self aware, growth oriented, has the aspiration to lead bigger teams and charters.

- 2+ years of experience in managing data pipelines / ETL jobs and other data systems for business intelligence, reporting, visualization.

- Deep, hands-on expertise in highly scalable distributed systems, service-oriented architectures and at least one object oriented language (preferably Java).

- Strong awareness of test automation tools and frameworks for API, DB, load testing

- Good understanding of data stores (SQL, No-SQL) for persistence and caching use cases, messaging queues, big data technologies (hadoop, HDFS, Hive, Spark, etc), streaming technologies, and prior experience in scaling these technologies.

Is a plus :

- Prior experience in machine learning, experience in managing development and operationalisation of machine learning projects is a strong plus.

- Prior experience with AWS (containers, data stores, caches, CDN, queues, monitoring, CI/CD, etc

(ref:hirist.tech)

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