Sr Data Engineer, Compliance Data Infrastructure at OKX

San Jose, California, United States -

Full Time

Start Date

Immediate

Expiry Date

28 Jun, 26

Salary

268333.0

Posted On

30 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Pipelines, Python, SQL, Spark, Hadoop, Databricks, Big Data Platforms, Cloud Platforms, Data Quality, Data Governance, AI-Assisted Engineering, LLM Tooling, Dimensional Modeling, Auditability, AML Transaction Monitoring, KYC/KYB

Industry

Financial Services

Description

Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity Good compliance analytics depends entirely on good data infrastructure. At a global crypto exchange, that infrastructure has to handle on-chain transaction data, fiat flows, KYC and KYB records, and behavioural signals across multiple jurisdictions, all in a form that data scientists and analysts can actually use. This role exists to build and own that foundation, and the quality of what gets built here directly shapes what the compliance function is capable of detecting. You will work closely with data scientists, ML engineers, analytics engineers, and compliance specialists as a core part of the team that delivers compliance intelligence at scale. The infrastructure you build is not internal plumbing; it is what enables detection models to run, self-serve analytics to function, and regulatory responses to be grounded in reliable, traceable data. AI-assisted engineering is how this team works. We use LLM tooling to accelerate pipeline development, automate documentation, improve code quality, and move faster on complex data problems. We are looking for a data engineer who already works this way and can bring that practice into a compliance data environment where reliability and auditability are non-negotiable alongside speed. What You’ll Be Doing Design, build, and maintain scalable data pipelines that ingest, transform, and deliver data for compliance analytics use cases including AML transaction monitoring, KYC/KYB, sanctions screening, and SAR reporting. Assess and improve existing data infrastructure: identify pipeline gaps, lineage issues, and data quality problems that silently degrade model and analytics output, and work through them systematically. Build and maintain compliance-specific data marts and semantic layers that allow data scientists and analysts to work independently, reducing the volume of ad-hoc data requests and increasing the team's overall throughput. Partner with data scientists and ML engineers to productionise feature pipelines, maintain data freshness, and build the infrastructure that keeps compliance ML models running reliably in production. Apply AI-assisted development as standard practice: using LLM tooling to write and review pipeline code, automate data quality checks, generate documentation, and accelerate debugging. The expectation is that you bring this fluency with you and use it to raise the quality and pace of your work. Implement data quality monitoring, pipeline health checks, and alerting that surfaces data integrity issues before they affect compliance decisions or model outputs. Work with compliance and legal teams to understand regulatory requirements around data retention, access control, and auditability, and build the controls that meet those requirements in practice. Support regulatory lookbacks and audit responses by ensuring historical data is retrievable, lineage is documented, and the evidence base the compliance team needs can be assembled accurately and quickly. What We Look For In You 8+ years in data engineering or a closely related role, with meaningful experience in financial services, fintech, or a compliance-adjacent environment. We welcome candidates across seniority levels; scope and responsibilities will be calibrated to your experience. Strong Python and SQL, with hands-on experience in distributed computing frameworks such as Spark, Hadoop, or Databricks. Solid experience with cloud and big data platforms including Alibaba MaxCompute, Google BigQuery, AWS Redshift, or equivalent, and a track record of building production-grade pipelines where reliability and data quality are treated as requirements rather than afterthoughts. Hands-on fluency with AI-assisted engineering. You use LLM coding tools regularly, have applied them to real data engineering work, and have a practical view on where they improve output quality and where they need careful oversight. This is part of how the team operates, not an optional extra. Experience designing data marts, dimensional models, or semantic layers for consumption by non-engineering stakeholders, with an understanding of what makes a self-serve analytics layer actually usable. A solid grounding in data governance, access control, and audit trail requirements in regulated industries, with an appreciation for why those requirements exist and how to implement them without creating unnecessary friction. Good communication skills, with the ability to translate data infrastructure decisions and constraints for compliance, legal, and business stakeholders who care about outcomes rather than technical architecture. Familiarity with the crypto ecosystem, on-chain data structures, blockchain analytics, or VASP regulatory frameworks is a meaningful advantage for this role and will give you useful context from day one. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! OKX Statement: OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. The salary range for this position is $223,611.00 to $268,333.00. The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via OKX internal or external careers site. #LI-HYBRID#LI-HYBRID #LI-ED1 Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX's Candidate Privacy Notice.

Responsibilities

The role involves designing, building, and maintaining scalable data pipelines to ingest, transform, and deliver data for critical compliance analytics use cases like AML monitoring and SAR reporting. This includes assessing existing infrastructure, building compliance-specific data marts, and partnering with data scientists to productionize ML feature pipelines.