Data Engineer

Addx Corporation; Colombia; Presencial; COP 381.934.000 - 509.246.000; Jornada completa

Descripción de la vacante

Addx Corporation is seeking a Data Engineer to build the data foundation for machine learning applications. You will ingest time-series data from real-time streams, integrate with various data sources, and collaborate with ML teams to develop reliable pipelines and clean datasets.

The role emphasizes production streaming/batch pipelines, strong software engineering practices, and data quality controls on AWS with Python as the primary language.

Formación

• Experience designing, implementing, and operating production streaming and batch data pipelines.
• Strong software engineering practices including maintainable code, automated testing, version control, and deployment.
• Practical understanding of streaming behavior, filtering, progress, buffering, rollback/replay.
• Experience organizing time-series, telemetry, or sensor data with schema evolution and efficient access patterns.

Responsabilidades

• Understand data sources, interfaces, formats, coverage, volumes, arrival rates, and quality; document findings for capacity planning.
• Build ingest pipelines for streaming and historical data across databases, APIs, files, and interfaces; implement configurable filtering.
• Shape data architecture by evaluating processing, storage, and access options against workloads and costs; document evolution.
• Deliver trusted datasets with consistent schemas, timestamps, units, identifiers, and source metadata; enable access for ML workflows.
• Operate reliable systems with repeatable deployment, monitoring, access controls, and data quality recovery procedures.

Conocimientos

Streaming pipelines

Python

AWS

Data modeling

Data quality

Herramientas

Airflow

Terraform

CI/CD

Descripción del empleo

Data Engineer: Streaming & Sensor Data

Location: Primarily Colorado Springs, CO; Denver, CO, also considered

We are seeking a Data Engineer to build the data foundation for our machine learning applications. You will integrate time-series measurements, including radar and OPIR data, from real-time streams, application databases, and systems with varying extraction capabilities. You will collaborate with senior technical staff and the ML team to characterize available data, establish reliable pipelines, and deliver cleaned, standardized datasets. You will help select technologies and develop an architecture based on measured workloads, application needs, and operational requirements. Our development environment is primarily Python, with AWS as the preferred cloud platform.

In This Role, You Will

• Understand our data sources. Investigate available interfaces, message formats, historical coverage, data volumes, arrival rates, and quality limitations. Document findings to guide implementation and capacity planning.
• Build ingestion pipelines. Develop maintainable workflows for streaming and historical data across databases, APIs, files, and application-specific interfaces. Implement configurable filtering to capture the messages and measurements needed downstream.
• Shape the data architecture. Evaluate processing, storage, and access options against representative workloads, reliability needs, interoperability, operational complexity, and cost. Document decisions and support the architecture’s evolution as requirements become clearer.
• Deliver trusted datasets. Establish consistent schemas, timestamps, units, identifiers, and source metadata while preserving measurement meaning. Implement validation and traceability, and define dependable access methods with the ML team. Support future feature, labeling, and training-data workflows.
• Operate reliable systems. Build tested, documented pipelines with repeatable deployment, monitoring, and appropriate access controls. Handle duplicate, missing, delayed, and out-of-order data, and implement recovery and reprocessing procedures.

We’re Looking For Someone With

• Experience designing, implementing, and operating production streaming and batch data pipelines and their supporting storage systems.
• Strong software engineering skills, including maintainable code, automated testing, version control, and deployment.
• Practical understanding of streaming behavior, including filtering, consumer progress, buffering, failure recovery, and replay.
• Experience organizing time-series, telemetry, sensor, or similarly complex data, with an understanding of schema evolution, partitioning, and efficient access patterns.
• Demonstrated ability to investigate unfamiliar systems, evaluate technical tradeoffs, and communicate recommendations while balancing reliability, performance, maintainability, and cost.

Additional Experience That Would Be Valuable

• Python-based data processing and AWS or comparable cloud infrastructure.
• Radar, OPIR, or other sensor measurement data.
• Workflow orchestration, containers, infrastructure as code, and automated delivery.
• Dataset versioning, feature generation, labeling, or training-data preparation.