Senior Data Engineer with Platform Experience
What you will be doing
Position Overview: We are looking for a Senior Data Engineer to design, build, and optimize our next-generation data platform. In this role, you will be the architect and builder of scalable data pipelines, ensuring our data ecosystem is reliable, efficient, and built for growth. You will collaborate closely with Tech Leads, analysts, and product teams to turn raw data into a strategic asset.
If you thrive in a collaborative environment, love optimizing complex data architectures, and believe that data pipelines should be treated with the same engineering rigor as production software, let's discuss.
Organization & Team: The role sits within the Core Data Platform team at People’s Bank. The team is entering a high-growth phase with multiple concurrent initiatives already underway and more starting in the coming months.
Key Responsibilities:
• Data Architecture & Modeling: Design and implement scalable, robust data models within our cloud data warehouse (Snowflake) to support diverse analytics and reporting needs.
• Pipeline Development (ELT/ETL): Build and maintain modular, tested, and well-documented data transformation pipelines using dbt (data build tool).
• Orchestration: Design complex workflow orchestrations and dependency graphs using Dagster or Apache Airflow, ensuring high availability and fault tolerance.
• Code Quality & Automation: Write clean, efficient, and reusable Python code for data functions and pipeline development and monitoring of data assets.
• Cloud Infrastructure: Leverage cloud services (AWS/GCP/Azure) to manage data lake storage, compute resources, and secure data sharing.
• Coding Best Practices: Perform code reviews, champion software engineering best practices (version control, testing, documentation) within the data team.
Tech Stack We Use: You don't need to be an expert in all of these, but this is what our environment looks like:
• Data Warehouse: Snowflake
• Transformations: dbt Core / dbt Cloud
• Orchestration: Dagster or Apache Airflow
• Language: Python, Advanced SQL
• Cloud Platform: AWS and Azure
• Infrastructure as Code: Terraform
• CI/CD: GitHub Actions
Scope of Work:
The Data Engineer will be expected to:
• Contribute to end-to-end project ownership, taking a defined component from requirements through delivery, with full accountability for quality and documentation
• Work on metadata storage strategy and log management, defining the right architecture and approach for internal data infrastructure
• Support multiple concurrent projects as the team scales, without requiring day-to-day supervision from the team lead
• Communicate directly with business stakeholders to gather and manage requirements, including raising scope changes proactively
• Follow and contribute to established development practices and pull request standards (dual approvals, documentation, test case evidence)
• Raise blockers early and bring solutions rather than waiting for direction
If you thrive in a collaborative environment, love optimizing complex data architectures, and believe that data pipelines should be treated with the same engineering rigor as production software, let's discuss.
Organization & Team: The role sits within the Core Data Platform team at People’s Bank. The team is entering a high-growth phase with multiple concurrent initiatives already underway and more starting in the coming months.
Key Responsibilities:
• Data Architecture & Modeling: Design and implement scalable, robust data models within our cloud data warehouse (Snowflake) to support diverse analytics and reporting needs.
• Pipeline Development (ELT/ETL): Build and maintain modular, tested, and well-documented data transformation pipelines using dbt (data build tool).
• Orchestration: Design complex workflow orchestrations and dependency graphs using Dagster or Apache Airflow, ensuring high availability and fault tolerance.
• Code Quality & Automation: Write clean, efficient, and reusable Python code for data functions and pipeline development and monitoring of data assets.
• Cloud Infrastructure: Leverage cloud services (AWS/GCP/Azure) to manage data lake storage, compute resources, and secure data sharing.
• Coding Best Practices: Perform code reviews, champion software engineering best practices (version control, testing, documentation) within the data team.
Tech Stack We Use: You don't need to be an expert in all of these, but this is what our environment looks like:
• Data Warehouse: Snowflake
• Transformations: dbt Core / dbt Cloud
• Orchestration: Dagster or Apache Airflow
• Language: Python, Advanced SQL
• Cloud Platform: AWS and Azure
• Infrastructure as Code: Terraform
• CI/CD: GitHub Actions
Scope of Work:
The Data Engineer will be expected to:
• Contribute to end-to-end project ownership, taking a defined component from requirements through delivery, with full accountability for quality and documentation
• Work on metadata storage strategy and log management, defining the right architecture and approach for internal data infrastructure
• Support multiple concurrent projects as the team scales, without requiring day-to-day supervision from the team lead
• Communicate directly with business stakeholders to gather and manage requirements, including raising scope changes proactively
• Follow and contribute to established development practices and pull request standards (dual approvals, documentation, test case evidence)
• Raise blockers early and bring solutions rather than waiting for direction