Remote Lead Data Engineer - Real-Time Aviation Analytics

Systems Enginuity Inc. (SEI); United States; Remote; USD 104,000 - 130,000; Full time

Benefits offered by this job

401(k)

401(k) matching

Dental insurance

Flexible schedule

Flexible spending account

Health insurance

Paid time off

Referral program

Vision insurance

Job summary

Systems Enginuity, Inc. (SEI) is seeking a Lead Data Engineer to build scalable real-time and batch data processing capabilities for FAA aviation safety research. You will lead the design, development, and deployment of pipelines handling aircraft trajectory, surveillance, and environmental data.

This role requires deep expertise in Python, SQL, streaming architectures, and modern data platforms. You will mentor engineers, drive data quality, and collaborate with data scientists on model

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, or a related field.
• 8+ years experience with a Bachelor’s degree, or 6+ years with a Master’s degree.
• Advanced proficiency in Python and SQL, plus current software engineering practices.
• Significant experience with streaming, event-driven, incremental, or distributed data processing architectures.
• Experience with Palantir Foundry, Kafka, Flink, Spark, Databricks, Snowflake, or comparable tools.
• Experience processing large-scale time-series, telemetry, sensor, geospatial, or spatial-temporal data.
• Experience with APIs, databases, cloud platforms, containerized environments, and modern software/data architectures.
• Experience with Java, TypeScript, or other languages used in large-scale distributed systems.
• Experience working with geospatial libraries and spatial indexing approaches.
• Experience with aircraft surveillance track data or ADS-B data, trajectory processing, and predictive analytics.
• Aviation, aerospace, transportation, or other safety-critical systems experience.

Responsibilities

• Lead the design, development, and deployment of scalable real-time/streaming, incremental, and batch data pipelines for aviation safety research and analytics.
• Design and implement data architectures to integrate and synchronize aircraft trajectory, surveillance, operational, environmental, and other spatial-temporal data.
• Create data models and processing approaches to maintain aircraft state, trajectory histories, spatial relationships, and derived analytical features.
• Develop and oversee processing capabilities that deliver reliable, reproducible inputs for trajectory prediction, anomaly detection, machine learning, visualization, and other analytics.
• Lead development of APIs, data services, feature pipelines, and interfaces connecting source data, analytical models, enterprise platforms, and user-facing apps.
• Improve engineering outcomes across performance, scalability, data quality, temporal alignment, spatial processing, and data gap handling for high-volume trajectory and telemetry data.
• Collaborate with data scientists and ML engineers on training data generation, model evaluation, feature engineering, and near real-time model execution.
• Provide hands-on technical leadership through architecture reviews, code reviews, engineering best practices, and mentoring senior and associate engineers.

Skills

Python

SQL

Streaming architectures

Event-driven processing

Distributed systems

Geospatial data

APIs

Cloud platforms

Java

TypeScript

Telemetry data

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Software Engineering, or related field

Tools

Palantir Foundry

Kafka

Flink

Spark

Databricks

Snowflake

Java

TypeScript

ADS-B

Job description

Systems Enginuity, Inc. (SEI) is seeking a Lead Data Engineer to build scalable real-time and batch data processing capabilities for FAA aviation safety research. You will lead the design, development, and deployment of pipelines handling aircraft trajectory, surveillance, and environmental data.

This role requires deep expertise in Python, SQL, streaming architectures, and modern data platforms. You will mentor engineers, drive data quality, and collaborate with data scientists on model