Staff Data Engineer
Role Overview
The Staff Data Engineer will lead enterprise-wide data architecture strategy and long-term data
platform design.
Evaluate and approve major technical designs, including establishing
engineering best practices.
Serve as technical advisor to leadership and influence product/data
roadmap.
Champion data governance, democratization, and scalable data standards.
Work with
cross functional teams including the Solutions Delivery, Business Intelligence; Predictive
Analytics, and Enterprise Services teams to assist with and support any data-related technical
issues and/or needs.
Analyze, manipulate, and process large sets of data using statistical
software.
Develop ETL (Extract, Transform, Load) and data pipeline solutions using Airflow,
DBT, Python, and Snowflake to load a data warehouse.
Design and manage ETL workflows to
support large-scale ingestion, transformation, and loading of data.
Build and scale CI/CD
pipelines and enterprise-wide data quality frameworks.
Test internal data pipelines for reliability
and performance.
Test, validate, and reformulate models to ensure accuracy.
Complete and
improve monitoring and validation processes to maintain pipeline reliability and data accuracy.
Write new functions or applications in programming languages to conduct analysis.
Build
streaming pipelines with Confluent Kafka for event-driven processing.
Build dashboards and
monitors for pipeline health, freshness, uptime, and SLA compliance.
Apply data modeling to
extract and analyze information from large structured and unstructured datasets.
Telecommuting
available anywhere in the United States.
Annual salary between $175,000 - $200,000.
Key Skills for This Role
Full Job Posting
Job Requirements
- Requires at least a Master’s degree or foreign equivalent in Data Science, Applied Computer
- Science, Computer Science or a related field. Must possess 4 years (48 months) of experience
- with all of the following: (a) developing and optimizing scalable pipelines using Apache Airflow
- and Python; (b) building streaming pipelines using Kafka; (c) tuning big data systems using
- Kafka and Hive; (d) using Spark to create jobs for batch and streaming analytics; (e) writing and
- using complex SQL queries to retrieve data from data warehouses for analysis and reporting (f)
- using AWS for data processing and large-volume workloads; (g) automating deployment of code
- and processes using Python; (h) working with cloud-based infrastructure; (i) building Datadog
- dashboards to monitor pipelines and system performance; (j) using Docker and Kubernetes to
- improve and validate containerization and automation reliability; (k) implementing Continuous
- Integration/Continuous Deployment (CI/CD) using Jenkins; and (l) utilizing all of the following:
- GitHub as a code repository, Windows and Linux operating systems. Experience may be gained
- concurrently. Telecommuting available anywhere in the United States.
About Unite Us
Unite Us is a private healthcare software company helping governments, providers, payers, and communities coordinate whole-person care.
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