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Data Integration Analyst

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Solidus Labs

Solidus Labs provides AI-powered trade surveillance and compliance solutions for the crypto and digital asset markets. They specialize in detecting market manipulation and fraud, helping clients manage risk and adhere to regulatory standards. The company also offers a trade surveillance course for compliance, risk, and regulatory professionals in both traditional and digital asset markets.

New York, USA

Projects

About Solidus Labs

Solidus Labs is a company focused on mitigating risk in the digital asset space through its comprehensive suite of compliance solutions. Their core offerings include trade surveillance and transaction monitoring, which leverage artificial intelligence to identify and address market abuse, fraud, and other forms of financial crime. The company has developed an "Agentic-Based Compliance" model to replace legacy systems, aiming to provide more effective and modern surveillance operations. In addition to their technology products, Solidus Labs offers professional services and educational resources such as a Trade Surveillance Academy and industry reports, positioning themselves as a key player in promoting integrity and safety within the crypto ecosystem. Their offerings also include Staking Guard and a training program covering topics like wash trading, spoofing, insider trading, and regulations like the EU's MiCA.

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Skills

About the Role

You will own the end-to-end onboarding and normalization of external data into the platform's standardized schema, aligning updated schemas where new paradigms are identified. You'll sit at the intersection of clients, data vendors, product, and R&D, translating heterogeneous source feeds into a consistent, load-ready format that powers downstream market inspection and surveillance algorithms. You'll move between technical specification and client-facing communication, ensuring external data is mapped faithfully, surfacing and resolving gaps early with R&D, and validating every feed end to end before it reaches production.

Requirements

  • Bachelor's degree in Information Systems, Industrial/Software Engineering, Computer Science, Data Analytics, or a related field, or equivalent practical experience
  • 5+ years in a data integration, data analyst, support, technical onboarding, or business/operations analyst role
  • High attention to detail and a structured, methodical approach to data mapping and validation
  • Strong analytical skills; comfort working with structured data, schemas, field-level mapping, and tabular formats (e.g., CSV, XML, JSON)
  • Excellent communication and stakeholder management skills
  • Strong service orientation and ownership
  • Exposure to data integration / ETL / adapter development workflows
  • Scripting or query experience (e.g., Python, SQL) for inspecting and transforming data
  • Experience with financial / market data or trading systems and their data requirements (nice-to-have)
  • Familiarity with Level 1 vs Level 2 market data and order/execution lifecycle concepts (nice-to-have)

Responsibilities

  • Onboard client trade data (orders and executions), specifying the normalized target format and transmission method (flat file / SFTP, API, Kafka JSON, FIX, etc.)
  • Map source fields to the standardized schema and align feeds to the standard load process
  • Coordinate and integrate market data feeds, scoping requirements per use case including Level 1 vs Level 2 depth
  • Align with vendors to attain data and map relevant fields to the standardized format
  • Surface schema gaps with relevant stakeholders
  • Handle other data feeds such as news/corporate events, client reference data, and other vendor feeds
  • Work the specification and adapter/mapping with the product team and R&D
  • Drive each integration end to end with R&D and the client from initial sample through validated ingestion
  • Confirm correctness in UAT before promoting to production
  • Maintain mapping documentation and source-to-target references
  • Monitor feed health and data quality and flag anomalies
  • Feed learnings back into the process