Engineering Manager, NIRA

About the role

The team manages engineering for NIRA and executes the product and infrastructure roadmap. Partners from product, data science, credit, and marketing collaborate to scale solutions for millions of customers.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Roadmaps are owned and executed to align delivery with business growth and user impact. Engineering practices for CI/CD, testing, reliability, and observability are raised to build reliable systems. Data infrastructure and internal tooling are built and maintained to support fast-moving product needs. Teams are led and mentored to grow high-performing engineers. Technical decisions are driven to balance speed, quality, and scalability. Partners work together to deliver measurable business outcomes.

Requirements

Candidates bring 7+ years of engineering experience with 3+ years in management roles. Experience leading teams within startup or scaling contexts is required. A strong record of improving engineering standards and delivery underpins the expectations for this role. Comfort operating across product, infrastructure, and data is necessary for success. Clear communication and a bias for action guide how work progresses.

Nice to have

Experience working in startup or scaling environments is preferred.

Practical notes

The role is based in Bengaluru, India, using a hybrid model. Direct exposure to leadership helps ideas move quickly within the team. Competitive compensation and generous equity reflect a focus on potential. Flexible health plans at multiple premium levels are supported by substantial global subsidies. Virtual-first collaboration spans 14 countries and 12 time zones, supported by a WFH office stipend. Global onsite events occur twice yearly in locations such as Vail, San Diego, and Mexico City. Growth follows contributions rather than rigid timelines, creating clear visibility for good ideas. Candidates are evaluated on potential, with ambition and drive valued over strict qualification checklists. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Engineering roles commonly use CI/CD pipelines, observability tools, and data infrastructure platforms. Mentorship and clear communication help ideas move quickly in fast-paced environments. Work often spans multiple time zones in global virtual-first teams.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.