Data Engineer, Mid

Acentra Health, LLC•UNAVAILABLE, UNAVAILABLE

About The Position

Acentra Health is looking for a Data Engineer, Mid to join our growing team. The purpose of this position is to design, develop, and maintain scalable data pipelines and systems to support the collection, integration, and analysis of healthcare and enterprise data. The primary responsibilities of this role include designing and implementing efficient data pipelines, architecting robust data models, and adhering to data management best practices. This role plays a crucial part in transforming raw data into meaningful insights, enabling data-driven decision-making across the organization. The ideal candidate will possess strong technical skills, a keen understanding of data architecture, and a passion for optimizing data processes.

Requirements

Bachelor's degree or equivalent experience in lieu of a degree
Proven experience as a Data Engineer or similar role with a focus on healthcare data.
Strong programming skills in object-oriented languages such as Python.
Proficiency in SQL.
Hands-on experience with Tableau, and/or Powerbi
Hands-on experience with data integration tools, ETL/ELT frameworks, and data warehousing concepts.
Hands-on experience with data modeling and schema design, including concepts such as star schema, snowflake schema, and data normalization.
Familiarity with healthcare data standards (e.g., HL7, FHIR), electronic health records (EHR), medical coding systems (e.g., ICD-10, SNOMED CT), and relevant healthcare regulations (e.g., HIPAA).
Hands-on experience with big data processing frameworks such as Apache Hadoop, Apache Spark, etc.
Working knowledge of cloud computing platforms (e.g., AWS, Azure, GCP) and related services (e.g., S3, Redshift, BigQuery).
Experience integrating heterogeneous data sources, aligning data models, and mapping between different data schemas.
Understanding of metadata management principles and tools for capturing, storing, and managing metadata associated with data models and semantic data layers.
Ability to track the flow of data and its transformations across data models, ensuring transparency and traceability.
Understanding of data governance principles, data quality management, and data security best practices.
Strong problem-solving and analytical skills with the ability to work with complex datasets and data integration challenges.
Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.

Responsibilities

Design and implement scalable and efficient data pipelines to acquire, transform, and integrate data from various sources, such as electronic health records (EHR), medical devices, claims data, and back-office enterprise data.
Develop data ingestion processes, including data extraction, cleansing, and validation, ensuring data quality and integrity throughout the pipeline.
Collaborate with cross-functional teams, including subject matter experts, analysts, and engineers, to define data requirements and ensure data pipelines meet the needs of data-driven initiatives.
Design and implement data integration strategies to merge disparate datasets, enabling comprehensive and holistic analysis.
Implement data governance practices and ensure compliance with healthcare data standards, regulations (e.g., HIPAA), and security protocols.
Monitor and troubleshoot pipeline and data model performance, identifying and addressing bottlenecks, and ensuring optimal system performance and data availability.
Design and implement data models that align with domain requirements, ensuring efficient data storage, retrieval, and delivery.
Apply data modeling best practices and standards to ensure consistency, scalability, and reusability of data models.
Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of healthcare data.
Develop and enforce data governance policies and procedures, including data lineage, architecture, and metadata management.
Collaborate with stakeholders to define data quality metrics and establish data quality improvement initiatives.
Document data engineering processes, methodologies, and data flows for knowledge sharing and future reference.
Stay up to date with emerging technologies, industry trends, and healthcare data standards to drive innovation and ensure compliance.
Read, understand, and adhere to all corporate policies including policies related to HIPAA and its Privacy and Security Rules.