Location: Brazil; Peru; Colombia; Argentina; Canada; Mexico; Chile
Workplace: Remote
BUILDING THE FUTURE OF OPEN FINANCE
Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.
THE TEAM
Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.
We’re looking for a Senior Analytics Engineer to join Kraken’s Finance Data Eng & Analytics team within Data Platform. You’ll build trusted financial data products, using automation and AI to reduce manual work and improve the close process.
Working at the intersection of data engineering and accounting, you’ll turn complex financial data into reliable pipelines, models and automated reconciliations. You’ll use AI-assisted tools to accelerate development and investigations, keeping financial outputs tested and traceable.
THE OPPORTUNITY
- You’ll work closely with Data Engineers, Finance and Platform Accounting to improve reporting and decision-making. Clear communication and collaboration will help you agree priorities, explain findings and drive adoption.
- Partner with Finance to understand workflows, identify pain points and translate business needs into practical data and automation solutions.
- Build and maintain automated workflows for data preparation, reconciliations, reporting and follow-ups, with clear controls, monitoring and exception handling.
- Build intuitive dashboards and visualizations that help stakeholders understand performance, investigate issues and make decisions.
- Resolve finance data issues and reconciliation breaks with Finance, Product and Engineering, addressing root causes and communicating progress.
- Build scalable, reliable pipelines to collect, transform and curate data from internal platforms and external systems.
- Support the design and rollout of a trusted finance data lake, with clear definitions, ownership and quality checks.
- Improve data timeliness, enabling near-real-time insights where they add business value.
- Evaluate new technologies, including AI-assisted automation, and prototype solutions to Finance needs that can be reliably supported in production.
WHAT YOU BRING
- 7+ years of experience in data analytics and data management within financial services or a regulated industry, working directly with Finance teams.
- Strong SQL and Python skills, including writing, debugging and maintaining production code.
- Solid experience with Git, merge requests, and code reviews, supported by strong software engineering practices, including testing and documentation.
- Hands-on experience building and operating pipelines and models using dbt, Spark, Airflow or comparable tools.
- Direct experience with finance or accounting data, such as reconciliations, subledgers, cost basis or month-end close, including investigating discrepancies and validating outputs with accounting stakeholders.
- A track record of owning pipelines end to end, making sound technical decisions and improving reliability as requirements and systems evolve.
- Judgment and autonomy to lead complex work, navigate ambiguity and remain accountable for production outcomes.
- Experience supporting recurring financial close activities, combined with a proactive approach to identifying manual processes and replacing them with reliable, sustainable automation.
- Excellent communication skills, with the ability to collaborate across Engineering and Finance, explain complex technical issues, articulate financial implications, and recommend actionable solutions.
NICE TO HAVES
- Experience with cryptocurrency transaction data and how digital-asset activity flows into financial records and reconciliations.
- Experience processing large volumes of financial transactions using distributed frameworks such as Spark or PySpark.
Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis.
Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution.
We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.
OUR COMMITMENT
Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto.
We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision.
As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws.
Stay connected
Follow us on Twitter https://twitter.com/krakenfx
Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address
Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/
Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice
УдалённоseniorBrazil; Peru; Colombia; Argentina; Canada; Mexico; Chile; We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance
Сбор данных из внутренних сервисов, LLM Gateway, GitLab, Jira, логов AI-агентов и других источников. Проектирование и наполнение баз данных и аналитических витрин для оценки использования CodeGen-платформы.
Организация пайплайнов потоков данных (конвейера движения данных в компании); Разработка, поддержка и оптимизация производительности Корпоративного Хранилища Данных (EDWH);
разработка и автоматизация пайплайнов репликации данных с Qlik Replicate (CDC, стриминг в Kafka/Redshift); настройка источников (SQL, NoSQL) и целей (data warehouse, облако);
Location: Remote, Global
About the Role
We're looking for a FinOps Data Engineer to help drive cost optimization and cloud financial governance across our AWS infrastructure and GCP footprint. You'll work cross-functionally with Engineering, Finance, and Product to find and ship savings, build the tooling necessary to maximize efficiency of every dollar spent in the cloud, and cultivate a culture of cost awareness across engineering teams. This role owns the data side of that mission: the models, pipelines, and analysis that make cost, usage, and margin numbers trustworthy across the company.
This is a hands-on engineering role, not an analyst seat. You'll do the schlep work other teams don't have time for, digging into a bucket's access pattern, writing the dbt model or Python pipeline that ships the fix, building the cost-vs-revenue mart that answers a margin question once instead of every time, and chasing a number until it reconciles. Strong communication matters as much as technical depth, you'll regularly explain a savings case or a data model's implications to an engineering team, a finance stakeholder, and a leadership audience in the same week.
What You'll Be Responsible For
In this role, you'll:
- Find and ship savings across AWS and GCP: Analyze billing, usage, and cost trends to identify opportunities, rightsizing, storage lifecycle policies, data transfer, and spot/auto-scaling. Take ideas from identification through to a realized dollar figure, not just a recommendation.
- Build and maintain the FinOps data pipeline: Design and run the BigQuery/dbt models that turn raw billing and usage data (CUR, Storage Lens) into reconciled, trustworthy mart tables, the foundation everything else in FinOps is built on.
- Build cost-vs-revenue and customer segmentation models: Model unit economics and margin by product line and customer segment, and extend coverage as new products and segments come online.
- Apply data science to cost and usage data: Build forecasting models, detect anomalies statistically rather than by threshold alone, and quantify savings opportunities (e.g. storage lifecycle, request-cost patterns) with rigor a spreadsheet can't match.
- Build and maintain cost attribution: Tagging policies, cost allocation, and chargeback/showback so engineering teams can see and own their own spend. This is foundational work other savings work depends on.
- Automate cost governance: Build automated cost reporting, anomaly detection, and alerting. Write the scripts and infrastructure-as-code (Python, Terraform, Lambda) that enforce cost policy rather than relying on manual review.
- Partner with engineering on architecture decisions: Bring cost into the room early, cost-efficiencies are cheaper to get right at design than to retrofit.
- Work with Finance on forecasting and reporting: Support budget tracking and financial KPIs; build dashboards and self-serve analytics engineering and finance can both read.
You Might Be a Good Fit If You
- Have strong AWS expertise and working GCP knowledge, including pricing models, billing structures, and cost optimization mechanics.
- Have data engineering depth: dbt, BigQuery (or an equivalent modern warehouse), SQL at scale, and data modeling for analytics (dimensional modeling, incremental models, testing).
- Have data science fundamentals: statistical forecasting, anomaly detection, and experimentation, and know when a rigorous model beats a rule of thumb.
- Have automation and scripting skills: Python, Terraform/ pulumi, Lambda, or Bash.
- Are comfortable with cost/billing tooling: AWS Cost Explorer, Cost & Usage Reports, GCP Billing Reports, and BI/dashboarding (we use Hex).
- Have experience with, or are comfortable picking up, Linear and Notion as the system of record for tracking and documenting FinOps work.
- Experience with a dedicated cloud cost management platform (e.g. CloudZero, Vantage, or similar) is a plus, not required.
- Can analyze cost data and present it clearly to both technical and non-technical audiences.
- Have prior experience spanning data analytics, data engineering, or data science in a FinOps, cloud cost management, or cloud governance context.
- Are energized by doing the unglamorous, high-leverage work (the schlep) that unblocks other teams, and by working cross-functionally without positional authority.
WHAT WE OFFER
- Fully Remote
We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world.
- ESOP
Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together.
- Tech Allowance
Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work.
- Health Benefits
Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us.
- Annual Off-Sites
Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year.
- Flexible Work
We operate asynchronously and trust you to manage your own time. You know what needs to be done and when.
- Professional Development
Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth.
ABOUT THE TEAM
Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love.
- ~400 team members
- 60+ countries
- 20+ languages spoken
- Over $1B raised (including our $500M Series F)
- 540,000+ community members
We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.
Задачи:
— вести сбор требований с бизнес‑стейкхолдерами
— проектировать модели данных по паттернам medallion‑архитектуры (bronze/silver/gold)
— строить и оптимизировать ETL/ELT‑пайплайны с dbt
— настраивать проверки качества данных (Great Expectations)
— оптимизировать Spark‑джобы под SLA
— диагностировать проблемы в многосистемных пайплайнах
— поддерживать документацию и runbook’и
Требования:
— от 5 лет опыта в Data Engineering
— продвинутое владение Python, Spark и SQL
— опыт работы с AWS и Databricks
— знание Kafka и инструментов современного Data‑стека (dbt, Airflow или аналогов)
— понимание dimensional‑моделирования (star/snowflake‑схемы)
— понимание принципов проектирования хранилищ данных и архитектур (lambda, kappa, lakehouse)
— умение структурировать задачи для работы с ИИ‑ассистентами
— знание топологий систем
— уровень английского — Intermediate и выше.
— Проектирование архитектуры данных: разработка и описание целевой архитектуры хранилищ (OLAP/OLTP), определение моделей данных, выбор форматов хранения и стратегий партиционирования. — Стриминг и батч-обработка: разработка стриминговых джоб на Apache Flink и батч-процессинга на Apache Spark.
🧩 Требования: от 2 лет опыта с Python и библиотеками для работы с данными (pandas, multiprocess, Google Cloud SDK); от 2 лет в ETL/ELT‑технологиях (REST API, GraphQL, DBT) и оркестрации пайплайнов (Airflow, Astronomer, DBT Cloud);
Ищем инженера данных с сильными компетенциями архитектора данных и баз данных, который сможет взять на себя планирование и проведение внедрения инструментов хранения и обработки данных.
Ищем инженера данных с сильными компетенциями архитектора данных и баз данных, который сможет взять на себя планирование и проведение внедрения инструментов хранения и обработки данных. • Опыт работы на позиции Data Engineer от 4 лет в сфере разработки ПО.
- Разработка Data Pipelines: проектирование и реализация ETL/ELT-пайплайнов: построение DAG в Apache Airflow, написание кастомных операторов и сенсоров на Python, настройка ретраев, алертинга и зависимостей. - Стриминг и батч-обработка: разработка стриминговых джоб на Apache Flink и батч-процессинга на Apache Spark.
Задачи:
— поддержка и развитие решения по обогащению данных на основе event sourcing
— построение витрин данных с использованием Airflow, dbt и Materialized Views в ClickHouse
— внедрение проверок качества данных
— автоматизация рутинных задач
— внедрение стандартов Data Engineering
— ведение технической документации
Требования:
— от 3 лет опыта в data engineering
— уверенное владение SQL (сложные аналитические запросы, оптимизация)
— сильный Python (фундаментальные знания и опыт с библиотеками для DE — pandas, SQLAlchemy, PySpark)
— опыт разработки production‑кода (ООП, архитектура приложений, тестирование, Git, CI/CD)
— практический опыт работы с Airflow (создание и поддержка DAG)
— опыт работы с dbt (модели, тесты, макросы)
— опыт работы с ClickHouse (движки таблиц, Materialized Views, тюнинг производительности)
— опыт работы с Kafka (продюсеры/консьюмеры, топики и партиционирование, гарантии доставки)
— опыт с инструментами контроля качества данных (dbt tests, Great Expectations, Soda)
— ориентация на результат и ответственность за итоги работы
