Lead Data Engineer

The core responsibilities for the job include the following:

Data Platform Leadership:

• Lead the development and continuous improvement of data pipelines and integration frameworks.
• Define standards for ingestion, orchestration, and data movement across the platform.
• Improve reliability, performance, and cost efficiency of data pipelines and storage.
• Design scalable, reusable patterns that support consistent data flow across systems.

Data Integration and Architecture:

• Design and implement integrations across internal and external systems using tools such as Fivetran and custom pipelines.
• Own how data enters and moves through the warehouse, ensuring consistency and integrity.
• Structure data layers to support both upstream flexibility and downstream consumption.
• Ensure strong alignment between source system logic and warehouse representations.

Data Modeling and Foundation:

• Architect and implement core data models in Snowflake using dbt.
• Build durable data layers aligned to core revenue and operational domains.
• Ensure strong alignment between upstream data structures and downstream reporting needs.
• Embed testing, documentation, and performance optimization into standard engineering practice.

Data Reliability and Quality:

• Establish and enforce data quality standards across ingestion and transformation layers.
• Design systems for validation, monitoring, and lineage.
• Proactively identify and resolve data issues, reducing ambiguity and downstream impact.
• Strengthen trust in enterprise data through consistent and reliable delivery.

AI-Enabled Data Systems:

• Treat AI as a core capability in modern data engineering, leveraging it to deliver high-quality solutions with greater speed and rigor.
• Design data pipelines and structures that enable safe and effective use of AI with trusted data.
• Integrate AI into engineering workflows to accelerate development, monitoring, and optimization.
• Apply disciplined judgment when evaluating AI-generated outputs, ensuring enterprise standards for accuracy and consistency.
• Establish practical guardrails for responsible AI usage across both engineering and business consumption.

Cross-Functional Partnership:

• Translate complex and ambiguous business questions into scalable data solutions.
• Communicate technical trade-offs clearly to both technical and business stakeholders.
• Introduce platform and process improvements and drive them through implementation.
• Influence data standards across the Data and Analytics function.

Requirements:

• 6+ years of experience in data engineering or analytics engineering.
• Strong experience with data integration, pipeline development, and warehouse architecture.
• Strong hands-on experience with Snowflake and dbt.
• Experience with data integration tools such as Fivetran.
• Experience working with cloud platforms such as AWS.
• Advanced SQL with demonstrated performance tuning expertise.
• Experience with Git-based workflows and GitHub for version control.
• Experience building and operating reliable data pipelines.

Skills

analytics engineering, data engineering, dbt, snowflake