Senior / Team Lead Customer Success Manager

About Nexla

Nexla is the leading Integration platform, built with AI, for AI. Nexla takes a metadata driven approach to converge diverse integrations across Data, Documents, Agents, Applications, and APIs into a single design pattern. We accelerate the development of solutions for GenAI, Analytics, and Inter-company data. Nexla makes data users and developers up to 10x more productive by delivering a true blend of no-code, low-code, and pro-code interfaces.

Leading companies including DoorDash, LinkedIn, Johnson & Johnson, and LiveRamp trust Nexla for mission-critical data. Named in the 2022, 2023, and 2024 Gartner Magic Quadrant™ for Data Integration Tools and top-rated by customers on Gartner Peer Insights, headquartered in San Mateo, California.

At Nexla, our culture is built around our core values:

Have Empathy

,

Be Curious

,

Be Intellectually Honest

,

Achieve Excellence

, and

Remember to Relax

. We put our customers at the heart of everything we do, foster a data-driven mindset, take ownership of our work, and believe in the power of teamwork to achieve ambitious goals.

Role, briefly

Our Customer Success team owns the end-to-end client experience, from initial integration through enterprise adoption and expansion. It is a small team, and this role does two jobs at: running your own enterprise accounts, and leading, mentoring, and coaching the CSMs around you. You work closely with Sales, Solutions, Engineering, and executive leadership, and you are the primary voice of the customer internally, including on where the platform goes next.

Our Customer Success team owns the end-to-end client experience, from initial integration through enterprise adoption and expansion. It is a small team, and this role does two jobs at: running your own enterprise accounts, and leading, mentoring, and coaching the CSMs around you. You work closely with Sales, Solutions, Engineering, and executive leadership, and you are the primary voice of the customer internally, including on where the platform goes next. As part of a lean and high-impact team, you will directly manage a portfolio of 10-15 strategic enterprise accounts, guiding them through both initial and long-term customer success.

What you’ll own

• Retention and expansion across your accounts. That starts at, overseeing complex implementations alongside our Solutions Engineers and Architects, including vector pipeline setup into existing customer infrastructure, and runs through renewals, upsells, and SOWs with Sales.
• The standard of work across the CS team. Coaching and mentoring the CSMs, and owning the playbooks, health-score frameworks, and retention strategies they run on. The function has to scale as the company grows.
• Executive relationships at your accounts. C-level sponsors, VP-level data owners, and AI steering committees, aligned on business value and ROI. You run the Executive Business Reviews and technical workshops.
• What happens when things go wrong. You are the escalation point for critical issues, technical roadblocks, and at-risk accounts, pulling in engineering and leadership and keeping it calm.

AI tooling is part of the job here, not a side interest. Customer communication, health tracking, and account reporting are all places we expect you to use it.

Ninety days : You will have fully your initial pod of strategic enterprise accounts, audited and leveled up our CS playbooks. You will have joined key customer calls to assess health across your 10–15 enterprise accounts, identified the next set of strategic and tactical deliverables for each, and established clear coaching cadence and updated playbooks for the team.

The hard problem

Because Nexla’s enterprise platform bridges deep data engineering (ETL/ELT, APIs, streaming) with cutting-edge AI architectures (agentic workflows, RAG, and MCP servers), the core tension in this role is balancing technical execution with high-level value creation.

Must-haves

• 12+ years in customer success management, engagement management, technical delivery, professional services, or sales engineering, in B2B SaaS.
• Background in solutions engineering, sales engineering, or technical consulting where you acted as a bridge between business teams and engineering.
• Experience establishing or scaling early-stage Customer Success frameworks (e.g., building playbook templates, health-score scoring systems, or churn-risk matrixes).
• Track record of identifying expansion opportunities (upsells/cross-sells) and collaborating closely with Account Executives/Sales to close them.
• You have led, mentored, or managed team members and driven team-wide KPIs.
• You have built, negotiated, and held relationships with C-suite and senior enterprise stakeholders.
• You have run high-stakes escalations and de-escalated risk inside complex enterprise dynamics.
• Real technical depth in data operations and data engineering pipelines (ETL and ELT), plus modern AI paradigms: GenAI, retrieval-augmented generation, vector databases, and LLM data flows.
• You use AI tools in your actual work and can say specifically where they help and where they mislead you.

You’ll thrive here if

• You lead from the front. You carry your own accounts while raising the standard of the people around you.
• You challenge the status quo rather than inherit it. The playbooks here are yours to rewrite.
• You are comfortable in a high-stakes executive conversation and in a technical about pipeline architecture, often on the same day.
• You can hold a lot of cross-functional threads at and still hit the dates you committed to.

One thing to be clear about: this is a small team, so you carry accounts as well as leading. If you are looking for a pure people-management role, this is not it.

How we hire

• Hiring Manager - VP Of Customer Success - Customer Engagement, Team and culture fit
• Lead Solutions Architect - Technical understanding
• CRO - Revenue understanding, cross selling & upselling, collaboration with Sales & PreSales
• Group Presentation - Lead a discovery, escalation or kickoff call with Stakeholders

The practical stuff

Bengaluru, hybrid, 2 days a week in office. Compensation includes base salary and equity, set by depth and experience rather than by title

• Working hours: the previous version of this role required US West Coast coverage.

Why Build Your Future at Nexla?

We are standing at the precipice of the GenAI revolution, but the biggest bottleneck isn't the models, it's the data. By joining Nexla, you aren’t just entering a company; you are stepping into the critical layer of the modern data stack that powers the AI economy. We are the

Data Fabric

that enables industry titans like

LinkedIn, DoorDash, and J&J

to turn messy, siloed data into ready-to-use products for RAG and predictive models. This is your opportunity to move beyond simple tooling and build the actual infrastructure that democratizes data access for the next decade of innovation. If you want to solve the hardest problems in data engineering and own a piece of a market projected to hit billions, your career belongs here.

• 12+ years in customer success management, engagement management, technical delivery, professional services, or sales engineering, in B2B SaaS.
• Background in solutions engineering, sales engineering, or technical consulting where you acted as a bridge between business teams and engineering.
• Experience establishing or scaling early-stage Customer Success frameworks (e.g., building playbook templates, health-score scoring systems, or churn-risk matrixes).
• Track record of identifying expansion opportunities (upsells/cross-sells) and collaborating closely with Account Executives/Sales to close them.
• You have led, mentored, or managed team members and driven team-wide KPIs.
• You have built, negotiated, and held relationships with C-suite and senior enterprise stakeholders.
• You have run high-stakes escalations and de-escalated risk inside complex enterprise dynamics.
• Real technical depth in data operations and data engineering pipelines (ETL and ELT), plus modern AI paradigms: GenAI, retrieval-augmented generation, vector databases, and LLM data flows.
• You use AI tools in your actual work and can say specifically where they help and where they mislead you.