Senior Data Engineer

Foodsmart

2d•$150,000 - $200,000•Remote

About The Position

The Senior Data Engineering is a critical role responsible for constructing and optimizing our data pipeline architecture, collaborating closely with data scientists and analysts to facilitate data-related functionalities. The Senior Data Engineer will be pivotal in designing, building, and maintaining highly scalable data pipelines, optimizing data delivery, and automating data processes. They will work closely with cross-functional teams to ensure efficient data flow and contribute to the success of our data-driven initiatives.

Requirements

6+ years of professional experience in data engineering, with a proven track record of leading large-scale, complex projects.
Deep expertise in modern cloud data warehouses (Snowflake, Redshift, or BigQuery).
Advanced SQL skills and strong experience with relational and columnar database systems.
Proficiency in data pipeline/workflow orchestration tools (e.g., Apache Airflow, Dagster, Luigi).
Solid programming background (Python, Scala, or Java) for data processing and automation.
Experience in backend development, with strong proficiency in TypeScript, Java, Python, Node.js, and SQL-based RDBMS such as PostgreSQL or MySQL
Experience designing data models and schemas for both operational and analytical use cases.
Strong analytical and problem-solving skills, thriving in fast-paced, high-growth environments.
Experience in backend development, with strong proficiency in TypeScript, Java, Python, Node.js, and SQL-based RDBMS such as PostgreSQL or MySQL
Bachelorâs degree in Computer Science, Engineering, Mathematics, or a related field (Masterâs degree preferred).

Nice To Haves

Familiarity with healthcare data standards (FHIR, HL7) or other regulated data domains is a plus.

Responsibilities

Lead the optimization and scalability of data delivery pipelines, ensuring performance and reliability for cross-functional stakeholders.
Architect, build, and maintain robust, high-throughput data pipelines and infrastructure to support advanced analytics and machine learning.
Partner with data architects, data scientists, and analysts to design solutions that meet evolving data requirements and business objectives.
Establish best practices for data quality, governance, security, and compliance; implement automated frameworks for cleaning, validation, correction, and enrichment.
Drive continuous improvement in data engineering processes by identifying bottlenecks, reducing manual effort through automation, and advancing scalability standards.
Mentor junior engineers, provide technical guidance, and influence broader data platform strategy.