Principal Data Engineer

DIRECTV•Los Angeles, CA

1d•Remote

About The Position

The Principal Data Engineer is a senior-level technical authority responsible for interpreting complex data analytics requirements and leading the design and development of sophisticated data products and solutions. This role serves as both a technical leader and mentor, guiding junior engineers and contractors while contributing strategic insights on data analytics, visualization, and engineering best practices. As an acknowledged expert on DIRECTV data technologies, the Principal Data Engineer bridges technical execution with business objectives, ensuring data solutions are scalable, secure, and aligned with organizational goals. This position requires deep technical expertise combined with the ability to solve complex, ambiguous problems through innovative approaches. Here's what you’ll do: Technical Leadership & Architecture Interpret complex requirements for Data Analytic Use Cases and translate them into actionable technical designs. Design and architect advanced data products, interfaces, and retention models with synthesis and anonymization capabilities. Serve as the technical authority and thought leader on data engineering best practices, tools, and methodologies. Lead the evaluation and adoption of new data technologies, frameworks, and approaches to enhance organizational capabilities. Make critical technical decisions that significantly impact data architecture and business outcomes. Drive innovation in data engineering solutions to address evolving business needs. Mentorship & Team Guidance Guide and mentor junior-level data engineers and contractors in the design and build of data products. Provide technical direction and code reviews to ensure quality, scalability, and adherence to best practices. Share knowledge and expertise through documentation, training sessions, and collaborative problem-solving. Foster a culture of technical excellence and continuous learning within the data engineering team. Serve as an escalation point for complex technical challenges faced by team members. Data Analytics & Visualization Contribute strategic insights on data analytics and visualization concepts, methods, and techniques. Collaborate with data analysts and business stakeholders to understand analytical requirements and use cases. Design data models and pipelines that enable efficient analytics and reporting capabilities. Optimize data structures and queries for performance and scalability across large datasets. Ensure data products deliver actionable insights that drive business decision-making. Data Governance & Security Work closely with CDO Policy and Security teams to create comprehensive data policy frameworks. Implement data governance standards including data quality, lineage, and metadata management. Design and implement data security measures including encryption, access controls, and anonymization techniques. Ensure compliance with regulatory requirements and company policies regarding data privacy and protection. Develop retention models that balance business needs with storage optimization and compliance requirements. Cross-Functional Collaboration Partner with product managers, analysts, and business stakeholders to understand data requirements and priorities. Collaborate with infrastructure and platform teams to ensure optimal data pipeline performance. Communicate technical concepts and recommendations to both technical and non-technical audiences. Lead cross-functional initiatives to improve data quality, accessibility, and utilization across the organization. Participate in strategic planning discussions regarding data platform evolution and capabilities. Problem Solving & Innovation Handle complex and ambiguous problems with innovative and strategic solutions. Integrate knowledge across multiple disciplines including data engineering, analytics, security, and business domains. Lead projects and initiatives with broad scope, receiving assignments in the form of objectives. Identify opportunities for process improvements, cost optimization, and technical debt reduction. Stay current with industry trends and emerging technologies in data engineering and analytics.

Requirements

5 – 7 years of progressive experience in data engineering, with demonstrated senior-level expertise.
Deep technical knowledge of data engineering technologies, tools, and best practices.
Proven experience designing and building complex data products, pipelines, and analytics solutions.
Strong understanding of DIRECTV data technologies, platforms, and business operations.
Expertise in data modeling, ETL/ELT processes, and data warehouse/lake architectures.
Hands-on experience with big data technologies, cloud platforms, and modern data stack tools.
Expert-level proficiency in programming languages such as Python, SQL, Scala, or Java.
Advanced knowledge of data processing frameworks such as Spark, Hadoop, or similar technologies.
Strong experience with cloud data platforms such as AWS, Azure, or Google Cloud.
Hands-on experience working with Databricks and Snowflake for large-scale data processing and analytics.
Familiarity with Databricks AI/BI capabilities and Snowflake’s AI and ML features (e.g., Snowflake Cortex) for accelerating insights and automation.
Proficiency in data visualization tools and techniques for analytics enablement.
Deep understanding of data security, privacy, and governance principles.
Experience with CI/CD practices, version control, and infrastructure as code.
Strong mentoring and guidance capabilities for junior resources and contractors.
Excellent communication skills with ability to explain complex technical concepts to diverse audiences.
Proven ability to lead technical initiatives and drive consensus across teams.
Experience serving as a technical authority and thought leader within an organization.
Collaborative approach with ability to build effective partnerships across functions.
Exceptional problem-solving abilities with creative approaches to complex challenges.
Strong analytical thinking with ability to integrate knowledge across multiple disciplines.
Ability to handle ambiguity and make sound technical decisions with incomplete information.
Strategic mindset with understanding of how technical decisions impact business outcomes.
Attention to detail with commitment to delivering high-quality, scalable solutions.
Deep understanding of data analytics and visualization concepts, methods, and techniques.
Knowledge of data governance frameworks, policies, and compliance requirements.
Experience with data anonymization, synthesis, and privacy-preserving techniques.
Understanding of data retention strategies and storage optimization approaches.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related technical field required.
Commitment to continuous learning and staying current with evolving data technologies.

Nice To Haves

Familiarity with media, entertainment, or telecommunications industry data challenges preferred.
Experience working with Finance and Marketing organizations is preferred.
Master's degree in relevant field preferred.
Relevant certifications in cloud platforms, data engineering, or related technologies a plus.
Recognized technical authority with ability to influence technical direction and standards.
Self-motivated with ability to lead projects independently with minimal oversight.
Innovative thinker who brings creative solutions to complex data challenges.
Strong business acumen with understanding of how data engineering supports organizational goals.
Proven track record of delivering significant impact on business outcomes through technical excellence.
Ability to balance technical perfection with pragmatic delivery of business value.

Responsibilities

Interpret complex requirements for Data Analytic Use Cases and translate them into actionable technical designs.
Design and architect advanced data products, interfaces, and retention models with synthesis and anonymization capabilities.
Serve as the technical authority and thought leader on data engineering best practices, tools, and methodologies.
Lead the evaluation and adoption of new data technologies, frameworks, and approaches to enhance organizational capabilities.
Make critical technical decisions that significantly impact data architecture and business outcomes.
Drive innovation in data engineering solutions to address evolving business needs.
Guide and mentor junior-level data engineers and contractors in the design and build of data products.
Provide technical direction and code reviews to ensure quality, scalability, and adherence to best practices.
Share knowledge and expertise through documentation, training sessions, and collaborative problem-solving.
Foster a culture of technical excellence and continuous learning within the data engineering team.
Serve as an escalation point for complex technical challenges faced by team members.
Contribute strategic insights on data analytics and visualization concepts, methods, and techniques.
Collaborate with data analysts and business stakeholders to understand analytical requirements and use cases.
Design data models and pipelines that enable efficient analytics and reporting capabilities.
Optimize data structures and queries for performance and scalability across large datasets.
Ensure data products deliver actionable insights that drive business decision-making.
Work closely with CDO Policy and Security teams to create comprehensive data policy frameworks.
Implement data governance standards including data quality, lineage, and metadata management.
Design and implement data security measures including encryption, access controls, and anonymization techniques.
Ensure compliance with regulatory requirements and company policies regarding data privacy and protection.
Develop retention models that balance business needs with storage optimization and compliance requirements.
Partner with product managers, analysts, and business stakeholders to understand data requirements and priorities.
Collaborate with infrastructure and platform teams to ensure optimal data pipeline performance.
Communicate technical concepts and recommendations to both technical and non-technical audiences.
Lead cross-functional initiatives to improve data quality, accessibility, and utilization across the organization.
Participate in strategic planning discussions regarding data platform evolution and capabilities.
Handle complex and ambiguous problems with innovative and strategic solutions.
Integrate knowledge across multiple disciplines including data engineering, analytics, security, and business domains.
Lead projects and initiatives with broad scope, receiving assignments in the form of objectives.
Identify opportunities for process improvements, cost optimization, and technical debt reduction.
Stay current with industry trends and emerging technologies in data engineering and analytics.