Data Integration Analyst

Description

At Solidus, we are shaping the financial markets of tomorrow by providing cutting-edge trade surveillance technology that protects investors, enhances transparency, and ensures regulatory compliance across traditional financial assets and crypto markets. 

With over 20 years of experience in developing Wall Street-grade FinTech, our team delivers innovative solutions that financial institutions and regulators worldwide rely on to detect, investigate, and report market manipulation, financial crime, and fraud. Headquartered in Wall Street, with offices in Singapore, Tel Aviv, and London, we safeguard millions of retail and institutional entities globally, monitoring over a trillion events each day.

Role Overview

The Data Integration Analyst owns the end-to-end onboarding and normalization of external data into the platform's standardized schema and aligning an updated schema where new paradigms are identified. The role sits at the intersection of clients, data vendors, product and R&D, translating heterogeneous source feeds — client trade data, market data, and other reference/vendor feeds — into a consistent, load-ready format that powers downstream market inspection and surveillance algorithms.

This role is characterized by a highly detail-oriented, analytical, and service-minded individual who is comfortable moving between technical specification and client-facing communication. Success means external data is mapped faithfully, gaps are surfaced and resolved early with R&D, and every feed is validated end to end before it reaches production.

Key Responsibilities

• Onboard client trade data (orders and executions): specify it to the normalized target format and transmission method (flat file / SFTP, API, Kafka JSON, FIX, etc.), map source fields to the standardized schema, and align the feed to the standard load process.
• Coordinate and integrate market data feeds: scope the requirement per use case (including Level 1 vs Level 2 depth), align with vendors to attain data, map relevant fields to the standardized format, and surface any schema gaps with relevant stakeholders.
• Handle other data feeds (news/corporate events, client reference data, other vendor feeds), working 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, confirming correctness in UAT before promoting to production.
• Maintain mapping documentation and source-to-target references, monitor feed health and data quality, flag anomalies, and feed learnings back into the process.

Responsibilities

Requirements

Must-Have

• 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; able to bridge technical and business conversations with both R&D and clients.
• Strong service orientation and ownership — able to drive an integration from specification through to validated, end-to-end delivery.
• Exposure to data integration / ETL / adapter development workflows.
• Scripting or query experience (e.g., Python, SQL) for inspecting and transforming data.

Nice-to-Have

• Experience with financial / market data or trading systems and their data requirements.
• Familiarity with Level 1 vs Level 2 market data and order/execution lifecycle concepts.

Why This Role Matters

The platform's surveillance and inspection capabilities are only as reliable as the data feeding them. Incorrectly mapped fields, the wrong market data depth, or schema gaps directly degrade detection quality. This role is critical because it ensures every external source — trade, market, and reference data — is normalized accurately, scoped correctly by entitlement, and validated end to end, so that the algorithms operate on trustworthy, consistent inputs.

Benefits