Pattern - Staff Data Engineer

Description :

As a Principal Data Engineer, youll be a technical leader and systems thinker within Patterns Data Engineering teamdesigning and scaling our canonical data model to deliver trusted, high-quality data.

Youll 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.

Youll lead through influence, shaping technical direction, mentoring engineers, and building the single source of truth that drives Patterns 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 :

- 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 :

- 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