About this role.
Able is hiring a senior engineer to design, operate, and improve a production knowledge graph that supports RAG and applied-AI workflows. The role centers on graph-focused ETL, large-scale ingestion, Kafka and CDC synchronization, temporal data modeling, data-quality automation, and Neo4j performance tuning. Candidates need at least four years of hands-on Neo4j experience, strong Cypher and Python skills, and a rigorous approach to schema design and entity resolution. This is a 40-hour, fully remote role restricted to Latin America, requiring strong written and verbal English communication. Finance, real-estate, and C# experience are beneficial but not required.
Role DNA
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Core skills
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Sample interview questions
I would begin by defining the target graph domain model, stable entity identifiers, relationship semantics, and data-quality rules. I would build staged ingestion that profiles and normalizes source data before resolving entities and loading idempotent batches into Neo4j. I would also include lineage, error handling, reconciliation metrics, and retry mechanisms so data issues can be diagnosed without corrupting the graph.
I would capture database changes through CDC, publish normalized change events to Kafka topics, and consume them with an idempotent graph-ingestion service. Each event would carry source identifiers, ordering or version information, and operation type to support inserts, updates, and deletes safely. I would monitor lag, failed events, duplicate processing, and reconciliation results to ensure graph freshness and correctness.
I would first profile representative Cypher queries and inspect execution plans to identify expensive scans, cardinality explosions, and poorly selective patterns. I would add or refine indexes and constraints, reshape queries to anchor on selective nodes, limit unnecessary traversals, and review the data model for overly dense nodes. I would validate improvements with realistic workload tests and track latency over time.
I would model the relationship or a dedicated fact node with effective start and end timestamps, source metadata, and a clear rule for current versus historical state. This preserves the full timeline while allowing efficient queries for a point in time or current ownership. I would define validation rules for overlapping intervals, missing end dates, and contradictory source events.
I would combine deterministic matching on trusted identifiers with probabilistic matching on normalized attributes such as names, addresses, and contact details. Potential matches would receive confidence scores and, where needed, be routed for review rather than automatically merged. Scheduled hygiene jobs would identify duplicates, orphan nodes, invalid relationships, stale records, and schema violations, with measurable quality dashboards and remediation workflows.
Back in 2012, we were a group of engineers and designers who decided we wanted to build things—so we did. Able started as an engineering and product hub building for a portfolio of early-stage startups. We built many relationships while developing products that were thoughtful, effective, and genuinely useful. But, since then, we’ve grown… and so has our ambition.
Now, we’re entering our next chapter—defined by applied AI. AI is a powerful force in the end-to-end software development cycle, and we’re creating practices that allow us to deliver software fast and more effectively than traditional approaches, creating meaningful value for our partners. Today, our builder mindset is driving us to become an AI-native organization across every function. We’re still evolving, and that’s part of the opportunity. If you want to build, learn, and tackle challenges alongside an ambitious team, let’s build together.
This position is 100% remote within LatAm.
What you’ll be doing
We are seeking someone who views the Knowledge Graph not just as a database, but as a living organism that requires constant care, feeding, and pruning. You understand that a RAG system is only as good as the data underlying it. You are intrigued by the complexity of ingesting massive, messy datasets and transforming them into clean, connected knowledge.
In short, someone who likes:
- Architecting Graph ETL: Designing and developing robust ETL pipelines specifically for graph ingestion. You aren’t just dumping rows into tables; you are determining how disparate data sources connect, evolve, and relate in a graph structure.
- Data Ingestion at Scale: Managing high-volume data streams using tools like Kafka and implementing CDC (Change Data Capture) patterns to ensure the graph reflects real-time reality.
- Automated Graph Hygiene: Writing scripts and jobs for deduplication, orphan node detection, and data consistency checks. You take pride in a clean schema.
- Modeling Time: Handling complex temporal relationships (e.g., how property ownership or financial status changes over time) within the graph.
- Performance Tuning: Ensuring that as the graph grows (25k+ reports and beyond), the underlying query performance remains snappy through optimizing indexes and storage.
What we’re looking for
We want to work with people who have a passion for collaborating with their teams, building software while nurturing inclusive and respectful relationships with their coworkers. With the ones that are open about their shortcomings and what they do not know now, but remain eager to keep on growing and closing those gaps.
Ideally, they would also have:
- Neo4j Expertise (Must Have): 4+ years of hands-on experience. You master Cypher, schema design, and the operational side of managing a production graph.
- ETL & Pipeline Mastery: Strong background in building data pipelines. You know how to take raw data, clean it, and structure it for graph ingestion.
- Streaming & CDC: Familiarity with event streaming platforms like Kafka and Change Data Capture methodologies to sync operational databases with the graph.
- Python Proficiency: Strong Python skills for writing ingestion scripts, maintenance jobs, and custom graph algorithms.
- Data Integrity Focus: Experience implementing automated jobs for entity resolution, deduplication, and quality assurance.
Nice-to-Have:
- C# Knowledge: Ability to read or contribute to C# codebases.
- Domain Experience: Prior work in Finance or Real Estate sectors.
Able is powered by curious, thoughtful people who care about what they build and how they build it. We’re actively investing in our team through AI training, knowledge-sharing, and hands-on experimentation to ensure everyone grows alongside the technology.
This position is 100% remote within LatAm. Strong verbal and written communication skills in English are a requirement. As a team member, you can expect:
- To work 40 hours per week, and be available during normal business hours as needed.
- Payments made in USD.
- 18 days of PTO per year, observance of local holidays, and an annual break between Christmas and New Years.
- Wellness + Remote Stipend
- AI Voucher
About Able
Able builds technology products in a portfolio model. We believe that people, teams, and processes are more important than the ideas themselves, so we’ve focused on bringing great people together, and investing in their growth.
We’ve built products in a variety of industries. Everything from media to finance to toys to healthcare. Sometimes we work with management teams to help their businesses grow faster or unlock value using technology. Other times we start or buy businesses outright. Each time, we look for opportunities to leverage technology built at the portfolio-level to drive value faster.
Able is committed to inclusion and diversity and is an equal-opportunity employer. All applicants will receive consideration without regard to race, color, religion, gender, gender identity, sexual orientation, national origin, disability, or veteran status.
This is but the beginning of a conversation we’d love to have with you.
Apply, and let’s get this adventure started!
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