> Markdown version of [/jobs/ext/3419043-sr-cloud-data-engineer](https://www.wearedevelopers.com/jobs/ext/3419043-sr-cloud-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Cloud Data Engineer - **Company:** ELLKAY, LLC. - **Location:** Elmwood Park, NJ, United States (Remote available) - **Experience:** Expert - **Salary:** $125,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Amazon S3, ASC X12 Standards, Apache HTTP Server, Audit Trail, Automation of Tests, Microsoft Azure, BigQuery, Cloud Computing, Cloud Database, Cloud Storage, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Warehousing, Distributed Data Store, Python (Programming Language), RabbitMQ, Reference Data, Standard Sql, Simple Data Format, SQL Databases, Data Streaming, Fast Healthcare Interoperability Resources, Snowflake, Apache Spark, Data Lakes, Kubernetes, Data Lineage, Health Level Seven International, Apache Kafka, Data Management, Api Design, Cerner EMPI, Amazon Simple Queue Service (SQS), Terraform, Data Pipelines, Docker, Amazon Redshift, Databricks - **Published:** September 2, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=279b005ffce20271 ## About the Role * 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. Technology Environment AWS / Azure / GCP * Python * SQL * Spark * Databricks * Airflow * dbt * Kafka * SQS/EventBridge * S3 / ADLS * Iceberg / Delta Lake * Snowflake / Redshift / BigQuery * Docker * Kubernetes * Terraform * HL7 * FHIR * X12/EDI Candidates do not need experience with every technology listed. Strong data-engineering fundamentals, cloud experience, and the ability to learn new technologies are most important. ## Description 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., * 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Making Data Warehouses fast. 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