Data Engineer Lead

AST SpaceMobile•Lanham, MD

About The Position

AST SpaceMobile 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. Position Overview 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

Requirements

Bachelor’s degree in computer science, data engineering, information systems, or a related technical field 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.
Strong interpersonal skills and ability to collaborate across cross‑functional teams.
Excellent written and verbal communication skills.
Strong problem‑solving, debugging, and prioritization abilities.
Ability to operate effectively in fast‑moving, ambiguous environments.
Meticulous attention to detail, ensuring accuracy across all documentation and data products.
Demonstrated ability to translate complex technical concepts for non‑technical stakeholders.
SQL (advanced proficiency for analytics‑grade modeling and transformations)
Python (data processing, automation, pipeline development)
Cloud platforms such as AWS (preferred), Azure, or GCP
ETL/ELT tools such as Airflow, Prefect, or Azure Data Factory
Modern data ecosystems including Databricks, Snowflake, Redshift, or similar
BI and analytics tools such as Power BI, Tableau, or Looker
Version control (Git), CI/CD, and modern testing frameworks

Nice To Haves

Master’s degree preferred but not required.
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 experience implementing data quality frameworks, metadata systems, or data lineage tooling.
Experience supporting enterprise analytics on a global scale.

Responsibilities

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.
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.
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.
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.