Data Platform Engineer -Cloud & Data Engineer

ELLKAY• US,

2d•$180,000 - $200,000•Hybrid

About The Position

ELLKAY is hiring a Data Platform Engineer to build and maintain production-ready data platform components supporting healthcare data management and interoperability solutions. The role focuses on reusable ingestion pipelines and connectors for files, relational databases, and APIs; transformations and source-to-target mapping for shared data models; data validation and quality; workflow orchestration; backend services; and cloud storage and processing patterns. The engineer will support reliable, observable, and cost-effective processing through logging, lineage, metrics, alerts, audit evidence, performance improvements, automated testing, deployment pipelines, technical documentation, and operational runbooks. The role collaborates with architects, DME, QA, security, and product teams. ELLKAY operates as a hybrid workplace, with remote work considered for candidates who live more than 40 miles from an office and whose skills and experience strongly align with the role.

Requirements

Proficiency in Python and SQL.
Experience developing production data pipelines and integrating databases, files, or APIs.
Experience with a major cloud platform, preferably AWS.
Understanding of data modeling, ETL/ELT, orchestration, and data quality.
Experience with Git, automated testing, CI/CD, and production troubleshooting.

Nice To Haves

Experience with Snowflake, Spark, lakehouse technologies, or large-scale data processing.
Experience with infrastructure as code, containers, and event-driven architectures.
Experience with healthcare data migration, HL7/FHIR, clinical data mapping, or archival systems.

Responsibilities

Build reusable ingestion pipelines and connectors for files, relational databases, and APIs.
Develop transformation and source-to-target mapping components for shared data models.
Implement schema validation, data profiling, quality checks, and source-to-target reconciliation.
Build workflow orchestration with dependency management, retries, checkpoints, and controlled reprocessing.
Develop backend services and APIs for processing requests, execution status, and delivery integration.
Implement cloud storage and processing patterns for raw, staged, curated, and archived data.
Add logging, lineage, metrics, alerts, and audit evidence to support troubleshooting and operational reporting.
Improve pipeline performance, infrastructure utilization, and processing costs.
Collaborate with architects, DME, QA, security, and product teams to deliver production-ready components.
Maintain automated tests, deployment pipelines, technical documentation, and operational runbooks.