Senior Data/Entity Resolution Engineer

About the Position

We are seeking a Senior Data/Entity Resolution Engineer to join our team at Fitch Solutions, focusing on building a scalable entity resolution system for federal court intelligence.

Project Description

Part of Fitch Solutions, we are the leading platform for federal court intelligence. We connect to 200+ federal court websites through PACER.gov, run thousands of automated data retrievals daily, and consolidate the information into a centralized database, enabling customers to search and monitor all federal courts from a single platform.

One key challenge remains: reliably identifying companies across court records. Party and case data is stored as freeform text without consistent identifiers, meaning the same company may appear under multiple name variations (e.g., IBM, I.B.M., International Business Machines), while subsidiaries may not include the parent company's name at all. As a result, customers must search countless name variations and can still miss relevant cases.

Your mission is to build an entity resolution layer that links a curated list of companies and their identifiers to party, docket, and filing data already in our database, providing customers with reliable, comprehensive results through a single search.

Responsibilities

• Design, build, and tune a Python-based entity resolution / record-linkage system that matches a curated company list against: 1) case data; 2) freeform party, docket, and filing text; and 3) external data that could assist in resolution.
• Produce results in a clear, reviewable, and de/serializable output form for validation before anything is written to production.
• Develop the data mapping logic that links many surface forms (abbreviations, punctuation variants, legal suffixes, aliases) to a single canonical company identifier.
• Solve the subsidiary / corporate-family problem: map related entities up to their parent identifier even when names share no common tokens, using reference data and curated mappings.
• Engineer matching against unstructured / freeform text: normalization, tokenization, fuzzy matching, blocking/candidate generation, and confidence scoring.
• Build deliberate precision controls to minimize false positives given sparse corroborating data including thresholds, scoring, ambiguity flags, and human-review queues for low-confidence matches.
• Write and run the internal script that persists approved identifier values to the correct records in the database.
• Define and track match-quality metrics (precision, recall, false-positive rate) and iterate to keep them reliable as the company list and case data grow.
• Partner with data, product, and engineering stakeholders to make the curated list, the matching rules, and the MDM approach maintainable over time.

Requirements

• Strong, production-level Python non-negotiable. You can build, structure, and maintain real data-processing code, not just notebooks.
• Hands-on experience with entity resolution, record linkage, fuzzy matching, or deduplication (e.g., libraries/approaches such as rapidfuzz, dedupe, recordlinkage, splink, or equivalents you can speak to in depth).
• Practical data mapping experience: normalizing and reconciling messy, inconsistent values into canonical forms.
• SQL proficiency and confidence working directly against a relational database to read source text and write results.
• Comfort working with unstructured / freeform text cleaning, standardizing, and matching real-world name data with all its noise.
• Solid MDM mindset: canonical identifiers, golden records, alias/cross-reference management, and why match quality and governance matter.
• A rigorous, precision-first instinct you treat false positives as a primary risk and can reason about precision/recall trade-offs and confidence thresholds.

Nice to Have

• Experience with legal, court, regulatory, or PACER/litigation data, or other domains with messy named-party data.
• Familiarity with corporate hierarchy / subsidiary reference data (e.g., LEI, DUNS, or similar identifier systems) and parent–child entity mapping.
• NLP techniques relevant to name matching phonetic algorithms, embeddings/vector similarity, named-entity handling.
• Experience designing human-in-the-loop review workflows for ambiguous matches.
• Building repeatable, schedulable batch jobs and clear match-quality dashboards or reporting.
• Background in risk, compliance, or financial information products (a natural fit with the Fitch Solutions mission).

We Offer

• Work your way – Enjoy the freedom to work from anywhere, with flexible hours that match your natural rhythm.
• Plenty of time to recharge – Take 15 paid vacation days, 10 additional unpaid days if needed, plus all national holidays.
• Meaningful, long-term projects – Dive into exciting 1–5+ year projects using the latest in AI, cloud and more.
• Support beyond the job – We help cover things like advanced language courses, gym memberships, and mentorship programs to help you grow.
• Work with global clients – Collaborate directly with international teams to create real impact.
• Make extra cash – Earn bonuses for referring great people or bringing in new business opportunities.
• Great people, no micromanagement – Join a supportive, results-focused team where you’re trusted to do your best work.

About the Company

This flexibility allows developers…

• A better work-life balance
• Increased productivity
• The ability to work any time around the clock
• Reduction in commute time
• Design your ideal daily schedule.
• Build a career, not just a job.
• Work smarter, not longer.
• More time with family and friends

Job Details

• Job Category: Data Engineer
• Job Location: LATAM/EUR
• Skills: Python, PostgreSQL, Opensearch
• Employment Type: Full-time
• Experience Level: Senior

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