Data Engineering Manager

About the role

You will own the end to end lifecycle of critical data platforms that power trading, investment banking, asset management, staking, self custody, and tokenization products across the firm. You will design and deliver scalable data infrastructure that connects institutions to digital assets while ensuring robustness, security, and performance. You will partner closely with product managers, data scientists, and infrastructure teams to translate business requirements into resilient data pipelines and services. You will lead the architecture decisions that shape how real time and batch data flow through the organization. You will mentor engineers, drive technical best practices, and foster a culture of ownership and continuous improvement. You will balance hands on technical leadership with people management to deliver ambitious data initiatives on demanding timelines. You will champion transparent feedback and high performance to ensure the team thrives in a mission first environment.

Key facts

What you'll do

Define and own the technical roadmap for data platforms that support trading, asset management, tokenization, and data center infrastructure for AI and HPC workloads.

Architect, build, and operate scalable data pipelines and services using modern data stack components, ensuring reliability, observability, and security.

Partner with product managers and business stakeholders to translate requirements into robust data models, schemas, and integration contracts.

Lead the design and implementation of real time and batch processing solutions that meet stringent latency, throughput, and availability targets.

Mentor data engineers and platform engineers, providing guidance on coding standards, testing, and production readiness.

Drive adoption of data quality frameworks, monitoring, and alerting to maintain healthy data ecosystems across the organization.

Collaborate with infrastructure and platform teams to optimize data workloads on cloud and on premises environments, including HPC and AI clusters.

Implement security and compliance controls for data access, ensuring alignment with internal policies and external regulations.

Evaluate and integrate new data technologies and tools to improve scalability, performance, and developer experience.

Own key service level objectives and work cross functionally to resolve complex data issues impacting business operations.

Champion engineering best practices such as version control, code review, documentation, and automated testing across data pipelines.

Translate business and product metrics into data strategies, ensuring that insights are actionable and support informed decision making.

Promote a culture of learning, experimentation, and continuous improvement within the data engineering organization.

Act as a technical leader in hiring, onboarding, and career development for data engineering professionals.

Requirements

Demonstrate strong technical leadership with a proven track record of building and scaling data platforms in production environments.

Bring hands on experience with data engineering tools, frameworks, and architectures used in trading, asset management, and digital asset businesses.

Showcase expertise in distributed systems, data pipelines, and database technologies, including both relational and NoSQL stores.

Exhibit deep knowledge of programming languages commonly used for data engineering, such as Python, Java, and SQL, along with scripting for automation.

Have a solid understanding of data warehousing concepts, including dimensional modeling, partitioning, and optimization for analytics workloads.

Possess experience with messaging and streaming platforms, such as Kafka, and related patterns for real time data processing.

Demonstrate familiarity with cloud platforms and infrastructure as code practices relevant to data workloads and observability.

Show strong collaboration and communication skills, with the ability to influence stakeholders and lead cross functional initiatives.

Nice to have

Experience in financial services, digital assets, or fintech environments where data integrity and regulatory considerations are critical.

Background working with AI and HPC data workloads, and familiarity with related infrastructure and tooling.

Exposure to tokenization, staking, or self custody product domains within a digital asset context.

Practical notes

The role is based in Mumbai and requires relocation to the site for the selected candidate.

Candidates must be eligible to work in India under applicable employment regulations.

Travel may be required within India and to other global offices as business needs dictate.

Visa sponsorship considerations will be evaluated on a case by case basis for roles requiring international mobility.

Candidates should be prepared for a fast paced environment with high performance expectations and transparent feedback.