Principal Data Engineer

About the Company:

Stable Kernel is a technology services firm and custom software developer building scalable software solutions for cutting-edge, innovative enterprises to move their business forward. We are headquartered in Atlanta, GA.

We’re a privately held, Great Place to Work Certified Company™ with a multi-award-winning culture and an impressive 10-plus-year trajectory of sustainable growth. At Stable Kernel, we support our employees in ways that help them do some of the best work of their lives.

About the Role:

As a Stable Kernel Principal Data Engineer, you play an essential role in setting our portfolio of world-class clients up for success through the development and delivery of their most innovative, transformational initiatives. You will collaborate daily with other engineers and product team members, make decisions that influence the path of a product roadmap, leverage software development best practices, and become a more well-rounded engineer as you learn new technologies. Your knowledgeable practice, reliability, and consultative nature make you an engineer that stakeholders and teammates trust.

Principal Data Engineers may be classified as individual contributors or people managers with individual contributor responsibilities.

Core Responsibilities Include:

• Technical Impact: 
• Designing and implementing exemplary solutions regarding scalability and cost-effectiveness by making trade-offs between opportunity and complexity.
• Setting standards for codebase health and promoting best practices throughout the organization. 
• Business Alignment: 
• Clarifying strategic outcomes and influencing roadmaps and projects.
• Identifying, suggesting, and driving improvements in your customers' end-to-end experience.
• Aiding in estimating work for new business with more unknowns and coaching others in estimation best practices. 
• Interacting with Others: 
• Coordinating across the entire company.
• Influencing the entire organization to make changes to support your work.
• Advising teams across the company.
• Autonomy & Ambiguity: 
• Designing a long-term roadmap with no direction. 
• Translating customer and business needs and strategic direction into projects and consistently simplifying high-complexity situations.
• Coaching and mentoring others in tackling ambiguity.
• Problem-Solving:
• Decomposing strategic direction into projects:
•  Planning, communicating, and executing to solve our most challenging problems.
• Ensuring alignment with long-term objectives, fostering a culture of informed decision-making and innovation.
• Anticipating most risks and driving simplification to mitigate risks ahead of time. 
• Escalating issues while solving them in parallel ensuring others are informed.
• Leadership:
• Proposing new organization-level processes to improve key areas such as team throughput, employee happiness, or product engagement.
• Driving best practices across the organization.
• Exhibiting exceptional mentoring abilities and fostering a culture of continuous learning and improvement by
• identifying and nurturing potential in others 
• providing strategic guidance
• helping develop career paths for team members 
• Leading projects.
• Setting the strategic direction for projects or areas of technology, leading multiple project teams, and influencing decision-making at higher organizational levels. 
• Exhibiting strong capabilities in stakeholder management, negotiating, and problem-solving in complex scenarios. 
• Mentoring and developing other leaders within the team, fostering a culture of innovation, and contributing significantly to organizational goals.

Intimate, Working Familiarity With:

• Extensive experience in Python.
• Designing and building data pipelines:
• Developing and maintaining scalable ETL/ELT pipelines using AWS services such as AWS Glue, Lambda, and Step Functions to ingest, process, and store data from various sources.
• Data Integration:
•  Integrating data from different sources, including on-premises systems and third-party APIs, into cloud-based data lakes or warehouses using AWS Data Pipeline or AWS Glue.
• Data Quality Management: 
• Implementing and enforcing data quality checks to ensure the accuracy, consistency, and reliability of data across systems.
• Collaborating with research teams to deliver research to production
• Collaborating with C