Analytics Engineer Jobs

About The Position

Requirements

• Strong proficiency in SQL and Python or similar programming languages, with experience in data engineering, data quality, data product development or application development, and data analytics.
• Hands-on experience building production data applications using AWS, Databricks, Spark, or comparable cloud and distributed-processing technologies.
• Deep experience building, monitoring, and optimizing large-scale data pipelines and data-intensive applications.
• Working knowledge of machine-learning workflows, including model development, deployment, evaluation, and monitoring.
• Experience with system design, data architecture design, integration, version control, CI/CD, testing, deployment, and production support.
• Experience with business intelligence tools such as Tableau or Power BI for reporting, data visualization, exploratory data analysis, and data analysis.
• Knowledge of metadata management, lineage tracking, data-quality frameworks, anomaly detection, testing, and monitoring practices.
• Strong analytical and problem-solving skills, including the ability to debug complex data issues and performance bottlenecks.
• Demonstrated ownership and accountability for design, development, testing, deployment, documentation, and ongoing support.
• Ability to learn new technologies quickly and communicate effectively across technical and business teams.
• Bachelor's degree preferred; relevant coursework and experience or extensive related professional experience may also be considered.
• 5–7 years of relevant work experience.
• Legally authorized to work in the United States without requiring employment sponsorship now or in the future.

Nice To Haves

• Familiarity with ML or data science models, AI-enabled analytics, LLMs, RAG applications, AI agents, or model tuning and fine-tuning workflows.
• Familiarity with workflow orchestration tools such as Airflow or similar technologies.
• Familiarity with networking, telecommunications, capacity management, or network deployment.

Responsibilities

• Design, develop, test, deploy, and support scalable data applications, planning tools, APIs, and data pipelines for network capacity planning and analysis.
• Build and optimize ELT and ETL processes across batch and streaming datasets.
• Develop automated data-quality checks, anomaly detection, master data management, metadata management, lineage, and monitoring processes.
• Develop, tune, and fine-tune machine-learning and AI models; evaluate performance and integrate models into production data applications and workflows.
• Automate recurring workflows, including orchestration, validation, monitoring, and issue notification, and provide system documentation.
• Perform ad hoc data analysis and support AI-assisted insight generation.
• Optimize and monitor data-intensive processes and applications, identifying and resolving performance, scalability, and data-quality issues.
• Create and maintain documentation for data pipelines, software specifications, system designs, processes, and support procedures.
• Apply sound practices for versioning, testing, privacy protection, cybersecurity, regulatory compliance, governance, responsible AI, and monitoring of data and AI solutions.
• Collaborate with network engineering, product, operations, software development, and data science partners to translate requirements into reliable technical solutions.
• Adapt to evolving business priorities, balance multiple initiatives, and maintain solution quality and delivery commitments.
• Proactively identify risks, dependencies, and improvement opportunities, escalate issues early, and drive resolutions independently.

Benefits

• Eligible employees receive an array of benefits; details are provided in Comcast's compensation and benefits summary.
• Most non-sales positions are eligible for a bonus.