Mid/Senior-level GoLang Data Engineer

Inca Digital, Inc.

2d•Remote

About The Position

Inca Digital is a veteran-owned data and intelligence company specializing in digital-asset analytics for exchanges, financial institutions, regulators, and blockchain ecosystems. Our technology and expertise provide clarity across crypto markets, tracking blockchain transactions, liquidity movements, and illicit finance, helping clients identify risk, enhance transparency, and improve decision-making. Inca’s infrastructure fuses structured and unstructured data from blockchains, exchanges, social networks, and financial markets. The result is a powerful analytics engine that supports ecosystem monitoring, market surveillance, and counter–illicit finance intelligence across digital-asset networks. Inca operates as a fast-paced, nimble, global, and remote technology company. We leverage an asynchronous‑first workflow, try to minimize time spent on meetings, and believe in open debate and logic over authority. Work alongside some of the sharpest minds in the world, including intelligence analysts, defense veterans, data engineers, quant researchers, linguists, and more.

Requirements

3+ years of professional experience developing backend services with Golang
Good knowledge of Go fundamentals, including concurrency, testing, etc
Experience with REST APIs and/or gRPC
Experience working in AWS environments
Experience with messaging systems such as Kafka/RabbitMQ/SQS/etc
Experience with SQL databases such as PostgreSQL
Experience with NoSQL databases such as MongoDB/Redis/DynamoDB
Familiarity with microservices and distributed systems
Experience with Docker, Git, and CI/CD

Nice To Haves

Experience with Terraform
Experience with Kubernetes
Familiarity with observability tooling and practices

Responsibilities

Design, build, and operate end-to-end batch and incremental ETL/ELT pipelines, turning raw data into production-grade APIs, dashboards, and automated alerts.
High-throughput data acquisition from diverse sources: relational databases, third-party and vendor APIs, flat files, blockchain nodes and indexers, and social and web text at scale.
Develop NLP pipelines working with unstructured and semi-structured text data — entity and claim extraction, classification, deduplication, and enrichment feeding downstream risk models.
Case management, report generation, and intelligence portal supporting data systems.
Partner with the Head of R&D, engineers, data scientists, and our investigations team to translate intelligence requirements into scalable client-facing products.
Contribute to technical design and architecture in a written, argued design process.
Support improving system data quality and system maintenance: versioning, validation, lineage, reconciliation, and freshness monitoring.