Staff Cloud Data Platform Engineer - Scalable Pipelines

Ellkay, Llc; United States; On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Smoke-Free Workplace

Job summary

ELLKAY is seeking a Staff Data Engineer – Cloud Data Platform to design, develop, and operate scalable cloud-based data pipelines and processing capabilities for its Data Platform and Data Lake. The role is hands-on, collaborating with directors, staff engineers, architects, and product teams to onboard healthcare workloads using reusable patterns.

You will build batch, streaming, and near-real-time pipelines, create ingestion capabilities for diverse data sources, and implement data layers with

Qualifications

• Strong hands-on experience in data engineering and cloud data platforms.
• Experience developing production-grade ingestion and transformation pipelines.
• Strong SQL and Python skills.
• Experience with AWS, Snowflake, Spark, or equivalent modern data technologies.
• Understanding of data quality, reconciliation, metadata, monitoring, and CI/CD.
• Experience working with large, complex, or regulated datasets.

Responsibilities

• Design, develop, and operate scalable cloud-based data pipelines and processing capabilities.
• Convert Data Platform architecture into production-ready ingestion, transformation, data-quality, and data-serving capabilities.
• Onboard healthcare data workloads through reusable platform patterns.
• Build scalable batch, incremental, streaming, and near-real-time data pipelines.
• Develop ingestion capabilities for databases, files, SFTP, APIs, healthcare messages, events, and partner data.
• Implement raw, standardized, curated, and product-serving data layers.

Skills

SQL

Python

AWS

Snowflake

Spark

Data pipelines

Cloud data platforms

Tools

CI/CD

Job description

ELLKAY is seeking a Staff Data Engineer – Cloud Data Platform to design, develop, and operate scalable cloud-based data pipelines and processing capabilities for its Data Platform and Data Lake. The role is hands-on, collaborating with directors, staff engineers, architects, and product teams to onboard healthcare workloads using reusable patterns.

You will build batch, streaming, and near-real-time pipelines, create ingestion capabilities for diverse data sources, and implement data layers with