Senior Analytics Engineer

About the role

This position is responsible for designing and maintaining the analytics infrastructure that powers institutional staking and yield operations for P2p.Org. You will own the data products that deliver transparent, reliable, and timely insights to clients such as exchanges and custodians around the world. The role requires translating complex on-chain and operational metrics into structured datasets and analytical models that drive business decisions. You will work closely with engineering and product teams to ensure data pipelines are scalable, observable, and aligned with strict institutional standards. A significant part of the role involves investigating anomalies, optimizing query performance, and ensuring data integrity across a high-scale crypto infrastructure. You will be expected to communicate technical concepts clearly to both technical and non-technical stakeholders during planning and review sessions. Success in this role is measured by the usability, accuracy, and reliability of the analytics products you deliver. You will play a key part in enabling P2p.Org to maintain its leadership position as the largest institutional staking provider with over $10B in TVL.

Key facts

What you'll do

Design and develop analytics datasets that provide clarity into staking yields, restaking performance, and client-specific reporting for institutional portfolios.

Collaborate with data scientists and product managers to define metrics that accurately reflect on-chain activity and institutional risk profiles.

Build and maintain data models in dimensional formats that support fast, consistent analysis across diverse client needs.

Implement robust data quality checks and monitoring to ensure that metrics remain accurate as blockchain protocols and business rules evolve.

Optimize SQL queries and data pipelines to handle increasing volumes of on-chain data without compromising system performance.

Partner with blockchain engineers to instrument new protocol features and capture the necessary data for immediate analytical consumption.

Lead investigations into data discrepancies by tracing issues through logs, query logic, and smart contract events to ensure audit readiness.

Document data definitions, pipeline behavior, and analytical methodologies to support long-term maintainability and team scalability.

Support the visualization stack by preparing datasets that enable dashboards to display real-time and historical insights with high reliability.

Contribute to the design of experiments that measure the impact of protocol changes on staking participation, yields, and fee structures.

Translate ambiguous business questions into structured analytical plans using SQL, Python, and domain knowledge in crypto infrastructure.

Mentor junior analysts and engineers on best practices for data modeling, version control, and testing in a high-availability environment.

Requirements

Demonstrate strong proficiency in SQL and the ability to write complex queries that perform well on large datasets.

Bring experience with Python for data transformation, scripting, and automation of analytical workflows.

Show competence in using modern data transformation tools such as dbt to manage version-controlled analytics code.

Have hands-on experience with workflow orchestration tools such as Airflow to schedule and monitor data pipelines.

Possess a Bachelor's or Master's degree in a quantitative or analytical field such as mathematics, statistics, computer science, or engineering.

Provide evidence of professional experience working with blockchain data, crypto protocols, or decentralized systems.

Exhibit a solid understanding of data modeling techniques, including dimensional modeling, normalization trade-offs, and time-series data patterns.

Communicate effectively in English at a B2 level or higher for documentation, code reviews, and cross-functional collaboration.

Demonstrate the ability to own end-to-end analytical products from requirements gathering through deployment and monitoring.

Show a track record of writing maintainable, tested code and documenting assumptions for future maintainers.

Display strong problem-solving skills and the capacity to break down ambiguous problems into actionable analytical steps.

Commit to working full-time in a fully remote contractor role with an indefinite-term consultancy agreement under a culture of ownership.

Nice to have

Experience contributing to open-source data projects or publishing reusable analytical packages.

Familiarity with modern visualization tools used to consume analytical datasets and build executive-facing dashboards.

Understanding of consensus mechanisms, cryptographic primitives, and incentive structures in proof-of-stake systems.

Background working with regulated financial institutions and compliance expectations around data governance.

Practical notes

The position is fully remote and operates on a full-time contractor basis with an indefinite-term consultancy agreement. Work is distributed across global locations, and the team operates under a culture of ownership and continuous learning.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

The role works with tools such as SQL, Python, Airflow, dbt, and modern visualization stacks in a high-scale crypto infrastructure environment. Team members own products from research through implementation, and professional growth is supported through education and conference opportunities.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.

About the company

P2P. org http://P2P. org is the largest institutional staking provider with a TVL of over $10B and a market share exceeding 20% in restaking.