Data Engineer Lead

AST Space Mobile is building the first and only global cellular broadband network in space to operate directly with standard, unmodified mobile devices based on our extensive IP and patent portfolio and designed for both commercial and government applications. Our engineers and space scientists are on a mission to eliminate the connectivity gaps faced by today’s five billion mobile subscribers and finally bring broadband to the billions who remain unconnected.

We are seeking a Lead Data Engineer to serve as a senior individual contributor—equivalent to a Staff or Principal Data Engineer—with full ownership of analytics data architecture and engineering standards. This hands‑on technical leadership role is part of a high‑impact analytics team responsible for enabling data‑driven decision‑making across satellite network planning, capacity and demand forecasting, network operations, and performance analytics.

The Lead Data Engineer will design, build, and operate scalable, production‑grade data pipelines and analytical infrastructure to ensure high‑quality; reliable data is consistently available across global planning and operational workflows. This role defines how operational, network, and business data is ingested, modeled, governed, and consumed—transforming complex, heterogeneous datasets into trusted, decision‑ready analytics assets.

While this position does not include people management, it carries significant technical ownership and influence. The Lead Data Engineer drives architectural strategy, establishes engineering best practices, and mentors analytics professionals to elevate data engineering maturity across the organization.

Success in this role is measured by enabling fast, confident, and consistent data‑driven decision‑making—not platform uptime alone. The focus is on delivering durable analytics foundations that support insight, alignment, and execution at scale.

Data Architecture & Platform Ownership

• Own the end‑to‑end analytics data architecture, including ingestion, modeling, governance, and consumption patterns.

• Design, build, and maintain scalable, reliable data pipelines supporting forecasting, network planning, and operational analytics.

• Establish and operate a lakehouse‑style architecture (raw → normalized → curated).
• Integrate diverse, complex operational and telemetry data sources into unified analytical and semantic models.

Analytics Enablement & Decision Systems

• Translate ambiguous business needs into durable data products, including curated datasets, semantic layers, and standardized KPIs.

• Define KPI frameworks with consistent definitions, calculations, and refresh logic across teams.

• Enable self‑service analytics by delivering trusted, well‑documented, discoverable datasets for BI and advanced analytics.

Data Quality, Reliability & Governance

• Implement automated validation, monitoring, and freshness checks across critical pipelines.

• Identify and resolve systemic data issues proactively, ensuring uninterrupted operational insights.

• Design schemas and pipelines with governance needs in mind, including lineage, auditability, and certification.

Technical Leadership & Standards

• Serve as the technical authority for analytics engineering and own architectural decisions.

• Establish and enforce engineering best practices, including testing, version control, documentation, and modular SQL/Python patterns.

• Mentor analysts and engineers to raise the quality and reliability of data products.

• Capture metadata and ownership for scalable governance and enterprise cataloging.

• Bachelor’s degree in computer science, data engineering, information systems, or a related technical field required.

• Master’s degree preferred but not required.

• A minimum of 7–10 years of experience in data engineering, analytics engineering, or related fields.

• Proven experience designing and operating production‑grade data systems at scale.

• Experience in telecom, satellite networks, IoT, or other high‑volume telemetry data environments.

• Familiarity with predictive analytics, forecasting workflows, or ML‑driven feature pipelines.

• Hands‑on…