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.