As a Principal Data Engineer, you will lead and design scalable data solutions, mentor engineers, and ensure data quality across the organization.
As a Principal Data Engineer, you’ll be a technical leader and systems thinker within Pattern’s Data Engineering team—designing and scaling our canonical data model to deliver trusted, high-quality data. You’ll transform complex, raw data into tables that are easy to understand and efficient to power analytics, forecasting, and AI/ML with a set of efficient pipelines. You’ll lead through influence, shaping technical direction, mentoring engineers, and building the “single source of truth” that drives Pattern’s global growth.
Roles and Responsibilities
- Design and evolve canonical and medallion-layer data models (bronze/silver/gold) that enable scalable, governed data across the organization.
- Build and optimize ETL/ELT pipelines using Apache Airflow, Spark, Trino, and cloud-native tools.
- Develop high-performance data marts and semantic layers that serve analytics and data science needs.
- Architect streaming and analytical systems using Kafka and ClickHouse for real-time and batch insights.
- Define and enforce standards for data modeling, documentation, quality, and lineage across all domains.
- Partner with Analytics, AI/ML, and Infrastructure teams to translate business logic into reusable, trusted data assets.
- Mentor engineers, lead design reviews, and drive continuous improvement in scalability and data reliability.
- Design and evolve canonical and medallion-layer data models (bronze/silver/gold) that enable scalable, governed data across the organization.
- Build and optimize ETL/ELT pipelines using Apache Airflow, Spark, Trino, and cloud-native tools.
- Develop high-performance data marts and semantic layers that serve analytics and data science needs.
- Architect streaming and analytical systems using Kafka and ClickHouse for real-time and batch insights.
- Define and enforce standards for data modeling, documentation, quality, and lineage across all domains.
- Partner with Analytics, AI/ML, and Infrastructure teams to translate business logic into reusable, trusted data assets.
- Mentor engineers, lead design reviews, and drive continuous improvement in scalability and data reliability.
Principal Engineer Leadership expectation
- Lead multiple engineering teams or initiatives, providing technical direction, coaching, and feedback to foster growth and high performance.
- Operate as a cross-team engineer and advisor, proactively identifying opportunities, dependencies, and risks across products and platforms.
- Drive high-impact architectural and process improvements, reducing technical debt and enabling long-term scalability, resilience, and operational excellence.
- Represent Data Engineering with executive stakeholders, helping set technical strategy and translating business needs into forward-looking data capabilities.
- Establish engineering best practices and advocate for robust design, testing, quality, security, and monitoring standards across teams.
- Lead incident resolution for critical data platform issues, and cultivate a culture of knowledge sharing and blameless postmortems.
- Evaluate and recommend adoption of emerging technologies where they drive measurable business impact.
- Scale your impact through effective delegation, enabling teams to deliver autonomously while maintaining technical rigor and clarity.
Technical Qualification
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.
- 10+ years of experience in Data Engineering, including 2+ years in a architectural-level technical role.
- Expertise in SQL, data modeling, and data mart design.
- Deep hands-on experience with Apache Airflow, dbt, Spark, Kafka, and ClickHouse.
- Proven experience designing medallion data architectures and scalable data lakehouse solutions.
- Proficiency in Python or Scala, and familiarity with AWS, GCP, or Azure data ecosystems.
- Strong understanding of data governance, lineage, and quality frameworks.
- Demonstrated ability to mentor engineers and influence architectural strategy across teams.
- Experience with real-time or streaming data (Kafka, Kinesis, or Pub/Sub).
- Knowledge of data observability and catalog tools (DataHub, Amundsen, Monte Carlo, Great Expectations, or Soda).
- Experience in eCommerce, retail analytics, or digital marketplaces.
- Exposure to governed data contracts and semantic layer frameworks.
- Proven track record of leading data architecture initiatives or cross-functional platform modernization.
- Contributions to open-source data tools or engagement in data community initiatives
Why Pattern?
The company is a rocket ship experiencing phenomenal growth
We have tailwinds and a long runway; we’re barely scratching the surface
We have big opportunities that will get you energized and excited
Great benefits including time off, insurance, competitive pay
Pattern provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability, status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
Top Skills
Apache Airflow
AWS
Azure
Clickhouse
GCP
Kafka
Python
Scala
Spark
SQL
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