About Zignal Labs
Zignal Labs’ real-time intelligence technology helps the world’s largest organizations protect their people, places, and position. Analyzing billions of data points in real time, Zignal's AI-powered platform accelerates mission-critical decision making by empowering leaders with contextual situational awareness of the information environment.
Fully remote, with Silicon Valley roots and team members in over 20 states, Zignal serves customers around the world. Learn more at .
About the Role
We ingest, enrich, and structure massive volumes of unstructured data — from social platforms and news outlets to broadcast media — and turn it into real-time intelligence for our customers.
As a Data Engineer on this team, you'll build and operate the pipelines that make that possible. You'll work on systems that process billions of events a day, and on the data pathways that feed our search, NLP, and AI services. You'll own meaningful pieces of the pipeline end to end, and you'll do it alongside engineers who have been running these systems at scale for years.
This is a hands-on build-and-operate role. You don't need to have designed a distributed system from scratch before — you need to be someone who writes solid code, reasons carefully about data correctness and failure modes, and wants to go deep on streaming and AI infrastructure.
What You'll Do
Build and maintain pipelines. Develop and operate batch and streaming pipelines that ingest and enrich high-volume unstructured data. Own components end to end, from implementation through production monitoring.
Support our AI systems. Build and extend the data pathways that feed downstream NLP, LLM, and retrieval services — including data preparation, embedding generation, and indexing workflows.
Work with search and storage layers. Integrate with and tune our search and vector stores to support semantic search, clustering, and real-time retrieval.
Build services and APIs. Implement and improve the microservices and APIs that deliver analytics to enterprise customers.
Operate what you build. Write clean, tested, maintainable code. Participate in code review, CI/CD, and infrastructure-as-code practices. Debug production issues and improve reliability over time.
Collaborate across teams. Work with Data Science, ML, Product, and Security to take ideas from prototype into production.
What You'll Need
These are the things we genuinely need on day one.
3+ years building and operating data pipelines in production.
Strong programming skills in a JVM language — Scala, Java, or Kotlin. Our core pipeline code is Scala. If you're strong in Java or Kotlin and want to learn Scala, we'll support that; we care more about your fundamentals than your current syntax.
Working proficiency in Python for data and scripting work.
Hands-on experience with a distributed processing framework, most likely Apache Spark.
Hands-on experience with a streaming platform, most likely Kafka — including a real understanding of consumer groups, offsets, partitioning, and what happens when things fall behind.
Practical AWS experience and comfort with Docker. You should be able to work in a Kubernetes environment; you don't need to administer one.
Experience with a workflow orchestrator such as Airflow, Prefect, or Dagster.
Solid SQL and experience with at least one NoSQL or caching layer (Redis, MongoDB, DynamoDB, or similar).
Sound CS fundamentals — data structures, algorithms, and the judgment to reason about performance and correctness in a distributed setting.
Strong written communication and the ability to work asynchronously across U.S. time zones. We're fully remote; writing clearly is part of the job.
Nice to Have
Genuinely optional. We don't expect any one candidate to have all of these, and we're prepared to teach them.
Databricks or Delta Lake specifically
Flink, or other stream-processing frameworks beyond Kafka
Vector databases (Pinecone, Qdrant, Milvus, pgvector) and hands-on RAG or embedding pipeline work
Elasticsearch or OpenSearch
Experience parsing messy, unstructured, or multilingual text at scale
Deep database performance tuning, or experience with distributed consensus systems
Bachelor's degree in Computer Science, Engineering, or a related field
Originally posted on