OSS Data Engineer

Position Overview

We are seeking a Data Engineer to build and operate the data platform spanning both network/operational (OSS) and business/commercial data. The person in this role asks what can be learned from the data our network produces, and who else, internally or at external partners, needs governed access to it.

You will own ingestion, storage, transformation, and serving for data ranging from raw network level telemetry streams to SLA/KPI reporting, whole-sale usage and settlements. The role combines real streaming-engineering depth with the judgment to keep network operational data and derived commercial data properly governed as they converge onto a single platform.

Key Responsibilities:

Build, develop and deploy data ingestion & streaming pipelines – Apache Flink, CDC-based ingestion, AWS Glue Schema Registry, in-stream validation and dead-letter handling

Design and maintain raw, curated, and served data layers, balancing query performance (Athena/Redshift) against storage cost and freshness requirements

Manage table maintenance, compaction, and partitioning strategy as data volume grows

Build and maintain SQL transformations from curated to served layers, including dimensional models that join multiple sources of data

Work on schema design for new data domains, translating target data models into working ingestion and transformation logic

Implement data quality testing covering freshness, completeness, and schema conformance across every pipeline

Build and maintain the BI/serving layer for internal stakeholders (dashboards, ad hoc query access, periodic reports)

Lay the data foundation, clean, governed, well-modeled datasets, for AI/ML use cases (capacity forecasting, predictive maintenance) without necessarily owning the models themselves

Maintain clear documentation of data models, pipeline architecture, and governance controls for both internal and partner-facing datasets

Engage with other internal teams to clarify requirements, validate assumptions, and ensure delivered data outputs and artifacts satisfy their needs

Qualifications

Education:

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field - or equivalent professional experience.

Experience:

A minimum of 4+ years of professional experience in data engineering, combining streaming and batch pipeline development, ideally including exposure to both operational/telemetry data and business/commercial data.

Production experience with Apache Flink or an equivalent stream-processing engine, including CDC-based ingestion

Hands-on experience with table formats such as Apache Iceberg or Delta Lake, and AWS Glue Data Catalog

Strong SQL and hands-on experience for building tested, versioned transformation pipelines

Experience with Protobuf and schema registries, and handling schema evolution without breaking downstream consumers

Experience operating Apache Airflow for batch pipeline scheduling

Working knowledge of S3, Glue, MSK, IAM, Athena/Redshift

Experience implementing access control and data protection for datasets with mixed sensitivity levels (operational vs. commercial data)

Proficiency in Python for pipeline tooling and glue code

English proficiency at B2 level or above; able to collaborate effectively with peers and architects in a globally distributed team

Soft Skills:

Comfortable being the seam between operational telemetry and business data, two domains that rarely share the same people or tooling

High ownership of data correctness for systems this role doesn’t operate, staying accountable to what the data says even when the underlying system belongs to another team

Clear communicator across analysts, architects, and partner-facing stakeholders with very different technical vocabularies

Proactive and self-directed in identifying where data quality or access-control gaps could cause real problems before they do

Technology Stack:

Apache Flink, Kafka/Protobuf (AWS MSK)

Amazon S3, Apache Iceberg, AWS Glue Data Catalog

Amazon Athena, Amazon Redshift

Apache Airflow, dbt, AWS Glue Schema Registry

OpenTelemetry, Prometheus, Grafana

SQL, Python

Git, Azure DevOps CI

Physical Requirements

Ability to work in a standard office or remote home-office environment and use a computer for extended periods

Ability to participate in occasional after-hours incident response actions

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.