Engineering Manager - Data

Coindcx; Bengaluru; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

Coindcx is seeking an Engineering Manager to strengthen the execution and people leadership of our Data Engineering organisation in Bengaluru. You will lead a team responsible for reliable, scalable data platforms supporting analytics, regulatory reporting, operations and AI/ML use cases.

You will translate the data strategy into predictable execution while building a high-performing engineering team, driving hiring, onboarding and capability development to sustain a strong engineering culture.

Qualifications

• 10+ years of software or data engineering experience, including 3+ years managing engineering teams.
• Strong experience building and operating large-scale data platforms or distributed systems.
• Hands-on understanding of data ingestion, batch and streaming processing, lakehouse or warehouse architectures.
• Experience managing platform roadmaps and cross-functional delivery.
• Operational excellence through observability, SLOs, incident management and root-cause prevention.
• Experience with cloud data tools and cost optimisation in a regulated environment.

Responsibilities

• Lead a Data Engineers team, coach, develop careers, manage performance and retention.
• Translate data strategy into quarterly plans with measurable outcomes.
• Own delivery predictability and governance across multiple teams.
• Establish planning, design reviews, operational reviews and incident management.
• Improve reliability, data quality, observability and cost controls for data products.

Skills

Team leadership

Delivery governance

Data engineering

Stakeholder management

Cloud platforms

Observability & SLOs

Tools

AWS

Databricks

Spark

Kafka

Airflow

Job description

We are looking for an Engineering Manager to strengthen the execution and people leadership of our Data Engineering organisation. You will lead a team responsible for building reliable, scalable and cost-efficient data platforms that support analytics, regulatory reporting, operational systems and AI/ML use cases. This role will translate the organisation's data strategy and platform roadmap into predictable execution while building a high-performing and engaged engineering team.

The candidate will have responsibilities across the following functions:

People and Team Leadership:

• Lead, coach and develop Data Engineers across multiple levels.
• Own performance management, career development, succession planning and retention.
• Drive hiring, onboarding and capability development.
• Build clear ownership, accountability and a strong engineering culture.
• Maintain team health through regular feedback, workload management and people pulse actions.

Delivery and Execution:

• Convert the platform roadmap into clear quarterly plans, milestones and measurable outcomes.
• Own delivery predictability, execution governance, dependency management and risk escalation.
• Coordinate execution across Product, Analytics, Finance, Risk, Security, Infrastructure and application engineering teams.
• Establish effective planning, design review, operational review and incident-management practices.
• Reduce unplanned work by addressing recurring incidents, operational gaps and manual dependencies.

Platform Reliability and Operational Excellence:

• Improve the reliability, availability, data quality and observability of critical data products and pipelines.
• Establish appropriate SLIs, SLOs, ownership and on-call practices for critical data services.
• Drive root-cause closure and ensure production learnings translate into engineering improvements.
• Strengthen security, governance, compliance and cost controls across the data platform.

Strategic Execution Priorities:

Partner with the VP of Data Engineering and Senior Staff Engineer to deliver three major priorities:

Data decentralisation and self-service:

• Enable domain teams to discover, onboard, publish and operate trusted data products.
• Establish clear ownership boundaries, data contracts, quality standards and reusable platform capabilities.
• Reduce dependency on the central Data Engineering team for routine data needs.

Platform cost transformation:

• Improve platform economics through workload optimisation and fit-for-purpose architecture.
• Support the transition from premium vendor-dependent solutions toward sustainable native and open technologies where appropriate.
• Establish cost visibility, accountability and unit economics for major workloads.

ML and AI platform enablement:

• Build the data foundations and engineering capabilities required for production AI/ML use cases.
• Partner with Data Science, Product and ML Engineering on data readiness, feature pipelines, governance and productionisation.
• Enable repeatable movement from experimentation to reliable production systems.

Requirements:

• 10+ years of software or data engineering experience, including 3+ years managing engineering teams.
• Strong experience building and operating large-scale data platforms or distributed systems.
• Hands‑on understanding of data ingestion, batch and streaming processing, lakehouse or warehouse architectures, orchestration and data quality.
• Demonstrated experience managing platform roadmaps and complex cross‑functional delivery.
• Strong people leadership across hiring, coaching, performance management and retention.
• Experience establishing operational excellence through observability, SLOs, incident management and root‑cause prevention.
• Ability to balance delivery speed, platform reliability, technical debt and cost.
• Strong communication and stakeholder-management skills.
• Experience with AWS, Databricks, Spark, Kafka, Airflow and modern lakehouse technologies.
• Experience building self‑service platforms or implementing data‑product/domain‑ownership models.
• Exposure to ML platforms, feature pipelines or production AI systems.
• Experience operating data systems in a regulated, financial‑services or high‑availability environment.
• Experience driving cloud or platform cost optimisation.