Position: Senior Data Engineer

Description

The Senior Data Engineer is responsible for designing, building, and operating production-grade data pipelines and data solutions that support analytics, automation, and operational workflows across the organization.

Working within a modern, cloud-native data stack, this role focuses on integrating data from internal systems and external partners, transforming that data into trusted and usable forms, and ensuring it is delivered reliably to downstream consumers. The role spans ingestion, orchestration, transformation, and delivery, with a balance of SQL-based data work and Python-driven pipeline development.

This position sits at the intersection of systems and data and partners closely with Business Intelligence, Business Technology, and operational teams to ensure data solutions are scalable, reliable, and aligned to real business needs.

Key Responsibilities

Core Data Engineering

• Design, build, and maintain scalable data pipelines that ingest data from internal systems, vendors, APIs, files, databases and cloud storage.
• Support bidirectional data movement between Snowflake and operational systems using APIs, flat files, webhooks, and other integration patterns.
• Profile, cleanse, and restructure data from disparate sources using dbt to produce well organized datasets for analytics, reporting, and operational use.
• Develop and maintain analytics ready tables and datasets that support both self-service analytics and downstream automation.

· Leverage Python and data engineering patterns to automate and streamline business workflows that extend beyond traditional analytics pipelines.

Orchestration & Platform Reliability

• Design and operate end-to-end workflows using orchestration tools such as Prefect, Dagster, or Airflow.
• Build and maintain event-driven and queue-based workflows where appropriate, extending beyond traditional batch ELT.
• Monitor pipelines, handle failures gracefully, and continuously improve reliability, observability, and performance.
• Identify and remediate data quality and pipeline performance issues across the platform.

Collaboration & Enablement

• Partner with BI Analysts, Business Systems Analysts and business stakeholders to translate business needs into scalable data solutions.
• Help to establish and evolve data engineering standards, patterns, and best practices.
• Participate in data governance efforts, including documentation of pipelines, datasets, and key business logic.
• Act as a senior technical resource and mentor for less experienced data engineers, contributing to skill development, technical decision-making, and continuous improvement across the data engineering function.

Required Qualifications

· 5+ years of experience in data engineering, analytics engineering, or related technical roles.

· 2+ years of experience working with Snowflake or similar cloud data warehouse platforms.

· 2+ years of experience using orchestration tools such as Prefect, Dagster, or Airflow.

· Strong experience working with SQL to transform, analyze, and structure data in a cloud data warehouse.

· Hands-on experience using Python to build data pipelines, integrations, and workflow automation.

· Experience designing and maintaining ELT/ETL pipelines in a modern data stack.

· Experience working in a CI/CD environment with Git-based version control.

· Hands-on experience using AWS services such as S3, Lambda, SQS, ECS/Fargate, or related tooling

· Comfort working with semi-structured data (e.g., JSON, APIs, file-based inputs).

· Strong communication skills and the ability to work effectively with cross-functional teams.

Preferred Qualifications

· Experience working with dbt for transformations, testing, and documentation.

· Experience supporting operational or automation-driven data workflows in addition to analytics.

· Experience working in regulated or compliance-sensitive environments.

· Experience with BI tools such as Sigma, Tableau, or Power BI.

· Auto finance or financial services experience.