Data Engineer - Sr.

Leading Fleet Mgmt. Platform is hiring Data Engineer - Sr. job (Remote only) | Cutshort

Data Engineer - Sr.
Leading Fleet Mgmt. Platform
Data Engineer - Sr.

at Leading Fleet Mgmt. Platform
Agency job
4
\- 8 yrs
₹20L - ₹45L / yr
Remote only
Skills
Data engineering
Apache Kafka
Spark
data engineer
Big Data
Python
Distributed Systems
Required Skills
Experience with various stream processing and batch processing tools (Kafka,
Spark etc). Programming with Python.
● Experience with relational and non-relational databases.
● Fairly good understanding of AWS (or any equivalent).
Key Responsibilities
● Design new systems and redesign existing systems to work at scale.
● Care about things like fault tolerance, durability, backups and recovery,
performance, maintainability, code simplicity etc.
● Lead a team of software engineers and help create an environment of ownership
and learning.
● Introduce best practices of software development and ensure their adoption
across the team.
● Help set and maintain coding standards for the team.
Read more
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Location: Mumbai, Maharashtra, India
Sector: Technology, Information & Media
Company Size: 500 - 1000 Employees
Employment: Full-Time, Permanent
Experience: 10 - 14 Years (Engineering Leadership)
Level: Engineering Manager / Group EM
ABOUT THIS MANDATE :
Recruiting Bond has been exclusively retained by one of India's most prominent and well-established digital platform organisations operating at the intersection of Technology, Information, and Media to identify and place an exceptional Engineering Manager who can lead engineering teams through an enterprise-wide AI adoption and digital transformation agenda.
This is a high-impact, hands-on leadership role at the nexus of people, product, and technology. The organisation is executing one of the most ambitious AI transformation programmes in its sector and this Engineering Manager will be a core driver of that change. You will lead multiple squads, own engineering delivery end-to-end, embed AI tooling and practices into the team's DNA, and shape the engineering culture of tomorrow.
We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.
THE OPPORTUNITY AT A GLANCE :
AI-First Engineering Culture :
– Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.
Hands-On Engineering Leadership :
– Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.
People & Org Builder :
– Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.
KEY RESPONSIBILITIES :
1\. Hands-On Technical Engagement :
– Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviews
– Set and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release quality
– Provide technical direction on backend platform strategy, API design, service decomposition, and data architecture
– Identify and resolve systemic technical debt and architectural risks across team-owned services
– Unblock engineers by diving into complex problems debugging, pair programming, and system analysis when it matters
– Own key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability
2\. AI Adoption, Integration & Transformation (2026 Mandate) :
– Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI features
– Champion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident response
– Embed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systems
– Lead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detection
– Partner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelines
– Build team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworks
– Create an experimentation culture : run structured AI pilots, measure productivity impact, and scale what works
– Stay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications
3\. People Leadership & Team Development :
– Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)
– Conduct structured 1 : 1s, career growth conversations, and development planning with every direct report
– Design and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026
– Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safety
– Drive performance management fairly and rigorously recognise top performers, manage underperformance constructively
– Lead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisions
– Contribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering
4\. Engineering Delivery & Execution Excellence :
– Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stability
– Implement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadence
– Drive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.
– Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trends
– Lead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvements
– Balance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership
5\. Strategic Leadership & Cross-Functional Influence :
– Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plans
– Participate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teams
– Represent engineering in leadership forums articulate technical constraints, risks, and opportunities in business terms
– Contribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure priorities
– Build relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesion
– Act as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI use
AI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :
In 2026, Engineering Managers at this organisation are expected to be active architects of AI transformation not passive observers. The following outlines the specific AI leadership expectations for this role :
AI Developer Productivity
– Drive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.
LLM & GenAI Product Features
– Own delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.
AI-Augmented Observability
– Implement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.
Team AI Fluency :
– Build mandatory AI literacy across all engineering levels.
– Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.
Responsible AI Governance :
– Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.
TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :
– Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)
– Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)
– AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHub
– Copilot: Cursor /Hugging Face
– Containers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)
– Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)
– Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/Sub
– MLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbt
– Observability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK Stack
– CI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)
QUALIFICATIONS & CANDIDATE PROFILE :
Education :
– B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferred
– Demonstrated engineering depth and leadership impact may complement institution pedigree
Experience :
– 10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership role
– Proven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS