Senior Lead, Data Engineering

Employment Type: Full-time Location Type: Remote Salary: Not Specified

Company Description

At Brightspeed, we are reimagining how people live, work, play and connect by providing fast, reliable internet connections and an awesome customer experience in twenty states throughout the Midwest and South. Backed by funds managed by Apollo Global Management, our vision is to accelerate the upgrade of copper to fiber optic technologies, bringing faster and more reliable internet service to many rural markets traditionally underserved by broadband providers, while delivering best-in-class customer experience. Be a part of the team that will make this vision a reality… designing and building a world-class fiber network and creating a customer experience second to none.

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Position Overview

We are looking for a Senior Lead, Data Engineering to join our growing team! In this role, you will play a key technical leadership role in the development of robust, scalable data pipelines and cloud-based data solutions to support enterprise-wide analytics and reporting. You are a highly technical individual contributor position with informal leadership responsibilities and architectural influence. You will lead the design and implementation of batch and real-time data pipelines using modern platforms such as Google Cloud Platform (GCP), BigQuery, Informatica, Hive, and Spark. Working closely with cross-functional teams, you’ll turn data into actionable insights while also mentoring peers and ensuring engineering best practices are followed.

Responsibilities

As a Senior Lead, Data Engineering, your duties and responsibilities will include:

• Design and implement efficient, reliable ETL/ELT pipelines for structured and unstructured data across cloud and on-prem environments.
• Build and maintain streaming and batch data pipelines using technologies like Kafka, Spark, and Dataflow.
• Develop data solutions that integrate with enterprise data warehouses (e.g., BigQuery, Snowflake, Hive) and data lakes.
• Lead technical designs for ETL architecture including data models, workflow orchestration, and performance optimization.
• Create scalable, reusable code and frameworks incorporating monitoring, exception handling, and version control.
• Collaborate with business analysts, data scientists, and engineering teams to translate business needs into effective data solutions.
• Support data governance and ensure data quality, consistency, and compliance with internal standards.
• Act as a technical mentor and guide for junior team members and data engineers.
• Proactively identify and resolve performance bottlenecks or design inefficiencies in data pipelines.

Qualifications

WHAT IT TAKES TO CATCH OUR EYE:

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.
• 8+ years of hands-on experience in data engineering, with significant experience in cloud and distributed data platforms.
• Strong experience with Informatica, Hive, Spark, Kafka, and Python.
• Proficiency in Google Cloud Platform (GCP) services (especially BigQuery, Pub/Sub, and Dataflow).
• Experience designing solutions using data warehouse modeling techniques (e.g., Star Schema, snowflake schema).
• Familiarity with orchestration tools such as Cloud Composer or Airflow.
• Strong SQL skills and experience working with large-scale datasets.
• Excellent problem-solving skills, ability to navigate ambiguity, and a high degree of initiative.
• Ability to communicate complex technical concepts clearly to technical and non-technical audiences.
• Experience in telecommunications or other high-data-volume industries.

BONUS POINTS FOR:

• GCP Professional Data Engineer certification or similar cloud certification.
• Experience with DataOps practices and CI/CD for data pipeline deployment.
• Experience in managing or supporting Hadoop ecosystems and cloud data migration.
• Knowledge of data security, scalability, and system performance tuning.
• Background in supporting machine learning workflows or feature engineering pipelines.