Senior Data Engineer

Convey Health Solutions•Fort Lauderdale, FL

3d•$120,000 - $135,000

About The Position

The Sr. Data Engineer drives the build, design and implementation of enterprise-grade data pipelines and platform components across healthcare use cases. With expert knowledge of data modeling, distributed processing (e.g., Spark, Airflow), and cloud-based architectures, they ensure that data infrastructure is secure, performant, and scalable. In addition to building, they champion code quality, observability, and CI/CD best practices using Git and Infrastructure as Code. They lead teams by example in sprint execution, backlog management, and inter-team communication—frequently interfacing with analysts, data scientists, and product owners to ensure alignment and shared understanding.

Requirements

Bachelor’s degree in data engineering, Computer Science.
Python & PySpark (6–8 yrs) – Designed reusable data pipeline frameworks and transformations for healthcare data.
Airflow (5–6 yrs) – Authored complex DAGs to orchestrate multi-stage ETL workflows across systems.
AWS Glue & Lambda (4–6 yrs) – Led implementation of serverless ingestion pipelines integrated with S3.
Iceberg (2+ yrs) – Modeled transactional lakehouse tables with schema evolution and time-travel support.
EMR Studio (2–3 yrs) – Used for pipeline prototyping, debugging, and notebook-based collaboration.
Docker/Kubernetes (3–5 yr) – Built and deployed containerized ETL pipelines using Docker; leveraged Kubernetes to orchestrate scalable data workflows in production environments

Responsibilities

Build enterprise-level scalable, low-latency, fault-tolerant data platforms that provide meaningful and timely insights.
Collaborate with a team of engineers to design and build data pipelines using big data technologies (Spark, Snowflake, AWS Big Data Services, Iceberg, Airflow) for medium to large-scale datasets.
Influence best practices for data pipeline design, data architecture, and processing of structured and unstructured data.
Automate and manage Git workflows, including CI/CD pipelines and Git hooks for data pipeline validation.
Design and deploy event-driven serverless functions using AWS Lambda, Step Functions, or API Gateway.
Implement and manage feature flag frameworks to support safe data product rollouts.
Identify and resolve data quality issues, including inaccuracies and incomplete information. Enhance data quality efforts by implementing improved procedures and processes.
Author detailed technical specifications and design documents for internal data products.
Work in a creative & collaborative environment driven by agile methodologies.
Continuously improve our products, systems, code, and team processes. Implement best practices and raise the bar by introducing new ones.