About the Role We are looking for an Engineering Manager to strengthen the execution and people leadership of ourData 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.

- 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 robust engineering culture.
- Maintain team health through regular feedback, workload management and people pulse actions.
- Convert the platform roadmap into transparent 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.
- 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.




- 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.
- 2. 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.

You ll Excel in This Role If You Have:

- 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.

You ll Know You re Winning When:

- Analytical Rigor: Exceptional problem-solving skills and attention to detail
- Technical Excellence: Ability to bridge quantitative research and engineering implementation
- Strategic Thinking: Can balance short-term tactical improvements with long-term strategic goals
- Collaboration: Excellent communication skills to work across quant, product, and engineering teams
- Adaptability: Thrives in fast-paced, dynamic crypto markets with rapidly changing conditions
- Ownership: Takes full accountability for market quality and strategy performance

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Engineering Manager - Data (Karnataka)
🏢 Coindcx
📍 Karnataka