Senior Data Engineer

Kilwa; Chicago (IL); Remote; USD 120,000 - 180,000; Full time

Job summary

Kilwa Technologies is hiring a Data Engineer to build and maintain the ingestion backbone across 54 markets and 85 languages. You will own pipelines for macroeconomic, policy, and news data, ensuring provenance and data quality at every step.

The role requires handling messy international sources and maintaining platform reliability with strong encryption. Location is Chicago or remote within the US, full-time in the Engineering team.

Qualifications

• Experience building production-grade data pipelines across diverse sources.
• Strong focus on data quality as a premier feature of the product.
• Ability to handle messy, multilingual data sources and formats.

Responsibilities

• Own ingestion pipelines across 54 markets and 85 languages.
• Maintain end-to-end provenance for inputs and model outputs.
• Ensure data processing meets reliability and security standards (AES-256).
• Operate the platform with robust monitoring and incident response.

Skills

Production data pipelines

Data quality mindset

International data sources

Tools

Airflow

Spark

Kubernetes

Job description

Build the ingestion backbone across 54 markets and 85 registered languages, where source coverage is uneven and provenance has to survive all the way to the score.

Location Chicago or remote (US)

Type Full-time

Team Engineering

What you'll do

• Own the pipelines that ingest macroeconomic, policy and news data across 54 African markets.
• Carry provenance end to end, so every input arrives at the model flagged verified or estimate.
• Make sparse and irregular source data tractable without silently imputing away the gaps.
• Operate the platform to institutional reliability standards, with AES-256 encryption in transit and at rest.

What you bring

• Deep experience with modern data infrastructure and orchestration in production.
• Comfort with genuinely messy international sources — inconsistent schedules, formats and languages.
• An instinct for data quality as a first-class product feature rather than a cleanup task.