Data Engineer 3 - Virtual

Job Description
What you can expect to do as part of this role
Architect, design, and build high-performance, scalable data pipelines that support large-scale data processing needs.

Develop complex Big Data ETL workflows using modern frameworks such as Databricks Delta Live Tables (DLT).

Design and implement robust data lake architectures, including open table formats such as Delta Lake and Apache Iceberg.

Define, promote, and enforce data engineering best practices to ensure data quality, integrity, reliability, and performance.

Lead the implementation of observability and monitoring across pipelines, including data cataloging, lineage, and metadata management.

Contribute to peer reviews, knowledge sharing, production support, incident resolution, and change management activities.

Adhere to code management and version control best practices across all development workflows.

Produce high-quality documentation including data flows, lineage diagrams, and architecture details, aligned to data engineering documentation standards.

Ensure comprehensive code coverage through the creation of unit and integration tests, following test-driven development (TDD) principles.

Own critical components of large-scale batch ETL systems and clearly articulate data models and design decisions to Product, Engineering, and Leadership stakeholders.

Manage multiple priorities effectively, balancing long-term architectural direction with immediate business needs.

Build advanced semantic layers and support the creation of customer-facing self-service analytics solutions.

Exercise independent judgment and discretion in handling complex and impactful technical decisions.

Maintain regular, consistent, and punctual attendance, with flexibility to work nights, weekends, or variable schedules as required.

Demonstrate strong understanding of semantic layer technologies such as Looker, Tab, Snowflake, and Databricks.

Perform additional duties and responsibilities as assigned.

Things that will come in handy:
Experience in Data Engineering.

Proficient in Python, PySpark. Good grasp on SQL.

Hands-on experience in Databricks and/or Snowflake is preferred

Data Warehouse modeling paradigms

Understanding of Cloud based architectures. We use AWS.

Problem solving skills

Certifications

AWS Certified Developer Associate

Databricks Certified Data Engineer Associate

SnowPro Advanced: Data Engineer

Responsibilities:
Designing and constructing data architectures and pipelines to standardize, transform, and ensure the integrity of data for business insights
Guaranteeing data quality throughout the ingestion, processing, and loading phases, with a focus on maintaining high standards
Engineering robust ingestion frameworks for a variety of data types, while monitoring and reporting on data quality
Developing accessible methods for data consumption, including APIs, database views, and data extracts for user applications
Implementing data solutions on multiple platforms such as Kubernetes, Teradata, and cloud services like AWS and Databricks
Selecting appropriate data storage platforms based on data sensitivity, privacy requirements, and access needs
Mastering data lineage and applying transformation rules for effective change management and issue resolution
Collaborating with cross-functional teams to refine data sourcing and processing, ensuring optimal data quality and efficiency
Consistent exercise of independent judgment and discretion in matters of significance.
Regular, consistent and punctual attendance. Must be able to work nights and weekends, variable schedule(s) as necessary.
Other duties and responsibilities as assigned.

Employees at all levels are expected to:
Understand our Operating Principles; make them the guidelines for how you do your job.
Own the customer experience think and act in ways that put our customers first, give them seamless digital options at every touchpoint, and make them promoters of our products and services.
Know your stuff be enthusiastic learners, users and advocates of our game-changing technology, products and services, especially our digital tools and experiences.
Win as a team make big things happen by working together and being open to new ideas.
Be an active part of the Net Promoter System a way of working that brings more employee and customer feedback into the company by joining huddles, making call backs and helping us elevate opportunities to do better for our customers.
Drive results and growth.
Support a culture of inclusion in how you work and lead.
Do what's right for each other, our customers, investors and our communities.

Education
Bachelor's Degree
While possessing the stated degree is preferred, Comcast also may consider applicants who hold some combination of coursework and experience, or who have extensive related professional experience.
Relevant Work Experience
5-7 Years

Skills Required
5-7 years relevant data engineering experience
Proficient in Python
Proficient in PySpark / Apache Spark
Strong SQL skills
Experience designing and implementing data lake architectures (Delta Lake, Apache Iceberg)
Experience building large-scale ETL/data pipelines and batch systems
Hands-on experience with Databricks or Databricks DLT
Hands-on experience with Snowflake
Familiarity with AWS cloud architectures and services
Experience with observability, data cataloging, lineage, and metadata management
Experience with semantic layer technologies (e.g., Looker) and supporting self-service analytics
Experience with Kubernetes and Teradata platforms (implementing data solutions)
Knowledge of data warehouse modeling paradigms
Bachelor's degree or equivalent coursework/experience
Ability to write unit and integration tests and follow TDD principles