Sr. Cloud Data Engineer

ELLKAY, LLC•Elmwood Park, NJ

4d•$125,000 - $150,000•Hybrid

About The Position

We are seeking a Senior Cloud Data Engineer to help build and scale our modern healthcare data platform. This hands-on role will design, build, and operate cloud-native data pipelines that ingest, cleanse, normalize, and deliver clinical and operational healthcare data at scale. You will work closely with architecture, engineering, data governance, QA, and product teams to modernize legacy point-to-point integrations into a secure, scalable, observable, and reusable data platform.

Requirements

5+ years of data engineering experience, including 2+ years building cloud-native solutions on AWS, Azure, or GCP.
Strong experience with Python and SQL and distributed data-processing technologies such as Apache Spark or Databricks.
Experience with orchestration and transformation tools such as Airflow, dbt, Prefect, or Dagster.
Experience with cloud storage/lakehouse technologies such as S3, ADLS, Delta Lake, Apache Iceberg, or Hudi.
Experience with a modern data warehouse such as Snowflake, Redshift, BigQuery, or Synapse.
Experience with streaming/event-driven technologies such as Kafka, Kinesis, SQS/EventBridge, RabbitMQ, Amazon MQ, or Azure Event Hubs.
Familiarity with Docker, Kubernetes, Terraform, CI/CD, and automated testing.
Understanding of data quality, de-duplication, master/reference data, security, and governance.

Nice To Haves

Strong data-engineering fundamentals, cloud experience, and the ability to learn new technologies are most important.

Responsibilities

Build scalable data pipelines for batch and streaming workloads using technologies such as Spark, Databricks, Airflow, and dbt.
Process healthcare data including HL7, FHIR, X12/EDI, CCD/CCDA, and flat-file formats.
Build cloud lakehouse solutions using S3/ADLS, Delta Lake, Apache Iceberg, or Hudi, along with modern cloud data warehouses i.e. Snowflake, Redshift
Improve data quality through automated validation, cleansing, de-duplication, schema validation, and quarantine workflows.
Support identity resolution and master data by integrating patient/member, provider, subscriber, and reference data with MPI/EMPI capabilities.
Modernize legacy integrations into reusable, configuration-driven, event-based, streaming, and API-based patterns.
Build for production reliability with monitoring, alerting, audit logging, data lineage, security, and operational observability.
Collaborate across teams to translate architecture into well-tested, production-grade solutions that meet healthcare security and compliance requirements.