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Senior Data Engineer

Skylo
CompanySkylo
CategoryEngineering
LocationBangalore
RemoteHybrid
EmploymentNot stated
LevelSenior
SalaryNot stated by the employer
Posted13 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
The world still has coverage blind spots. You could help eliminate them at Skylo.   Skylo has pioneered a standards-based approach to satellite connectivity. We connect smartphones and IoT devices directly to satellites. No special hardware, no entirely new networks. Just billions of existing devices, suddenly reachable anywhere on Earth. We're not building toward this future. We're already in it.   Our direct-to-device service is live on millions of activated devices across five continents, covering more than 72 million square kilometers, in partnership with leading satellite operators, mobile network operators, Tier-1 chipset makers, and OEMs worldwide. And we're just getting started.   At the heart of it all is Skylo's commercial NTN vRAN: a 3GPP standards-based, cloud-native platform that seamlessly bridges terrestrial and satellite networks. It's the infrastructure that makes true anywhere, anytime connectivity possible.   When you join Skylo, you'll work at the intersection of three markets reshaping how the world stays connected: mass-market consumer devices, automotive, and industrial IoT. Enabling people outdoors and critical workflows in the world's most remote places.   This is a rare chance to work on technology that matters, at a company that's already proving it works This role is located in Bangalore, India where we are onsite 3 days a week in office.   About the role The Enterprise Systems & Security group delivers the BSS, OSS, IT, security, cloud, and data infrastructure that the rest of the company runs on. Within it, the Cloud, Data & AI function owns enterprise data engineering and the data foundations for Skylo's AI initiatives. As a Senior Data Engineer, you'll design and build the pipelines, models, and platforms that turn raw data — from our OSS/BSS systems (CRM, billing, NetSuite), network elements, and operational tooling — into trusted, queryable, well-governed datasets. Your work directly enables business reporting, network observability, and the AI/ML use cases Skylo is investing in. This is a hands-on, build-it role with a high degree of ownership. What you'll do - Design, build, and operate batch and streaming data pipelines that ingest data from backend systems, network/RAN/core systems, and SaaS tools into a centralized cloud data platform. - Model data for analytics and operational use — define schemas, build curated/conformed layers, and maintain data marts that serve reporting, observability, and AI/ML consumers. - Build and maintain the data warehouse/lakehouse on cloud infrastructure (e.g., BigQuery / GCP, with AWS in the mix), including partitioning, performance tuning, and cost optimization. - Implement data quality, lineage, and governance — validation, monitoring, alerting on pipeline health, and documentation so downstream teams can trust and find data. - Partner with the AI & Innovation and analytics stakeholders to prepare feature-ready datasets and reliable data access for AI/ML initiatives across the enterprise. - Embed security and privacy into data systems — appropriate access controls (IAM), handling of sensitive subscriber and billing data, and compliance with relevant data-protection requirements. - Collaborate across the India, Finland, and US teams (OSS/BSS, Cloud Infra, Network Operations, business stakeholders) to translate requirements into scalable, maintainable data solutions. - Establish engineering best practices — CI/CD for data, infrastructure-as-code, testing, and code review — and mentor more junior engineers as the function scales. Required qualifications - 5+ years of professional data engineering experience building production data pipelines and platforms. - Strong SQL and proficiency in at least one programming language for data work (Python preferred; Scala/Java acceptable). - Hands-on experience with a cloud data warehouse / lakehouse (BigQuery strongly preferred; Snowflake/Redshift/Databrick
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