Pick a job to read the details

Tap any role on the left — its description and apply link will open here.

Data Engineer Lead

AST SpaceMobile · Lanham, Maryland, United States

Telco AST - Lanham Posted May 6, 2026

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

Key Responsibilities

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.

Qualifications

Education

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

Master’s degree preferred but not required.

Experience

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.

Preferred Qualifications

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.

Soft Skills

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.

Technology Stack

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

Physical Requirements

Ability to work in a standard office environment and use a computer for extended periods.
Occasional virtual or in‑person collaboration across global teams.

This job description may not be inclusive to the duties and responsibilities listed. Additional tasks may be assigned to the employee from time to time or the scope of the job may change as needed by business demands.

AST SpaceMobile is an Equal Opportunity, at will Employer; employment is governed on the basis of merit, competence and qualifications and will not be influenced in any manner by race, color, religion, gender, national origin/ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, mental or physical disability or any other legally protected status.

Ready to apply?

Apply to AST SpaceMobile

AST SpaceMobile

View all jobs →

Data Governance Senior

AST SpaceMobile · Lanham, Maryland, United States

Apply now

Telco AST - Lanham Posted May 6, 2026

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

The Data Governance Manager ensures end‑to‑end governance of satellite network data by establishing and maintaining standards, lineage, controls, and data‑quality frameworks that support reliable analytics for BPMS, RAN planning, mobility modeling, and regulatory reporting. This role defines data policies, validates data integrity, manages catalogs and lineage, enforces compliance, and ensures datasets meet engineering and planning requirements for accurate forecasting and operational decision‑making. The position reports to the Director of Network Planning & Analytics.

This role is responsible for ensuring the trustworthiness, consistency, and governance of all satellite‑network data used for planning, modeling, and regulatory submissions. The Data Governance Manager establishes governance standards, lineage practices, and quality controls that enable dependable analytics across BPMS, RAN planning, and mobility modeling. By enforcing data‑quality and compliance standards, the role underpins accurate forecasting, defensible regulatory reporting, and sound operational decisions at scale.

Key Responsibilities

Data Governance Framework & Policy Management

Establish data governance models, principles, and operating processes for satellite‑network datasets.

Define and enforce data policies, standards, classification rules, lifecycle guidelines, and governance controls.

Integrate governance requirements into planning workflows, data pipelines, and change management processes.

Metadata, Lineage, Cataloging & Data Quality

Maintain data catalog, glossary, metadata repositories, and end‑to‑end lineage for critical planning and mobility datasets.

Ensure data discoverability, semantic consistency, and traceability across engineering and analytics systems.

Set data quality SLAs/SLOs, implement automated checks, and lead remediation for issues impacting planning accuracy or regulatory reporting.

Compliance, Risk Management & Cross‑Functional Alignment

Ensure data compliance with regulatory reporting, internal governance, and audit requirements.

Maintain audit‑ready artifacts and evidence of lineage, access governance, and data controls.

Define stewardship roles, collaborate with planning, engineering, and analytics teams, and align data definitions with system‑level needs.

Analytics Enablement, Change Control & Reporting

Certify trusted datasets for BPMS analytics, RAN planning, mobility modeling, and forecasting.

Govern schema changes, version control, and data lifecycle evolution across platforms.

Deliver dashboards and reporting on data quality, lineage completeness, control adherence, risks, and remediation status.

Deliverables

Data governance charter and operating model; documented policies, standards, and control catalogs.

Enterprise data catalog with business glossary, certified data products, and end‑to‑end lineage maps.

Data quality framework with rules, SLAs/SLOs, scorecards, monitoring, and remediation workflows.

Stewardship model with ownership matrix (RACI), steward roster, and council meeting cadence notes.

Approved data models and definitions for BPMS, RAN planning, mobility modeling, and regulatory datasets.

Access governance artifacts (classification, RBAC/ABAC matrices), audit logs, and evidence packs.

Change management procedures covering schema/versioning, impact assessments, and rollback plans.

Quarterly governance dashboards (DQ trends, lineage completeness, incidents, SLA adherence).

Regulatory reporting data packs with traceable lineage, control evidence, and sign‑off records.

Deployment readiness “data certification” checklists for market activation and ongoing operations.