Sr. Cloud Data Platform Engineer in at Ellkay LLC
Explore Related Opportunities
Job Description
ELLKAY started out providing connectivity solutions to laboratories and within a few years, grew to also provide data management solutions to ambulatory organizations. ELLKAY is now a trusted data management partner in five healthcare segments. ELLKAY’s solutions continue to serve laboratories and ambulatory practices and have expanded to empower hospitals and health systems, healthcare IT vendors, ambulatory practices, health plans, and other healthcare organizations with cutting-edge technologies and solutions that drive their growth and interoperability strategies.
Today, ELLKAY remains true to our core values, building strong partner relationships and offering unparalleled service and support while providing innovative, scalable solutions to the challenges our customers face in today’s data-rich world.
ELLKAY's experience, customer-focused approach, and reputation for innovation, speed, and accuracy differentiate ELLKAY as a premier partner for your interoperability needs and data management strategy.
Job Description:
The Senior Cloud Data Platform Engineer will help design and build ELLKAY’s shared cloud data platform foundation. The role will focus on the platform services required to reliably ingest, orchestrate, process, monitor, and operate healthcare data workloads across AWS, Snowflake, and other enterprise data systems.
This engineer will build reusable infrastructure, APIs, workflow orchestration, deployment patterns, and operational capabilities that can be shared across data migration, interoperability, analytics, archival, data quality, and AI/ML use cases.
Essential Duties & Responsibilities:
- Design and build cloud-native platform services supporting data ingestion, processing, transformation, validation, and delivery.
- Develop the platform control plane and APIs used to configure, trigger, monitor, retry, and manage data workflows.
- Implement durable workflow orchestration for long-running and failure-prone data operations using technologies such as Temporal or equivalent services.
- Build reusable ingestion and connector patterns for databases, files, APIs, object storage, and healthcare data sources.
- Develop backend services using Python, Java, or Golang and expose platform capabilities through well-defined REST APIs.
- Establish Kubernetes, Helm, GitOps, and infrastructure-as-code standards for deploying and operating Data Platform services.
- Implement automated provisioning using Terraform, Ansible, and related cloud automation technologies.
- Design workflows for high availability, idempotency, resumability, retry management, and disaster recovery.
- Establish observability standards using metrics, logging, distributed tracing, dashboards, synthetic monitoring, and actionable alerts.
- Define service-level objectives, operational runbooks, support procedures, and incident-management practices.
- Partner with data engineers, architects, security, SRE, and product teams to establish secure and reusable platform patterns.
- Optimize cloud infrastructure for performance, scalability, reliability, and cost.
- Mentor engineers and help establish engineering standards across the Data Platform organization.
Qualifications:
- 8+ years of experience in software engineering, cloud engineering, platform engineering, or distributed systems.
- Strong programming experience in Python, Java, or Golang.
- Experience building backend services, microservices, REST APIs, and distributed systems.
- Hands-on experience with AWS or Azure cloud platforms.
- Strong experience with Kubernetes, Docker, Helm, and infrastructure as code.
- Experience building workflow orchestration, automation, or control-plane services.
- Knowledge of relational and NoSQL databases, SQL, object storage, and data movement patterns.
- Experience designing reliable systems with retry, recovery, idempotency, and high-availability requirements.
- Strong understanding of monitoring, observability, operational readiness, and production support.
- Ability to lead technical design and work across multiple engineering teams.
- Experience with Snowflake, Amazon S3, AWS Glue, EMR, Databricks, Spark, or similar data-platform technologies.
- Experience with ETL/ELT pipelines, data lakes, data warehouses, or large-scale data-processing systems.
- Experience with Temporal, Airflow, Dagster, Step Functions, or another workflow orchestration framework.
- Experience with Kafka or event-driven architectures.
- Familiarity with data governance, lineage, access control, data quality, and metadata management.
- Experience working with healthcare data, HIPAA-regulated systems, HL7, FHIR, or clinical data platforms.
- Experience applying AI-assisted engineering or specification-driven development practices.
Awards: