Data Engineer

ProArch•Atlanta, GA

11d

About The Position

The Data Engineer will be responsible for architecting and implementing scalable data pipelines, designing end-to-end data solutions, and ensuring data governance and compliance. This role involves collaborating with stakeholders to translate business requirements into robust data architectures, optimizing system performance, and ensuring data security. The engineer will also develop and maintain Power BI semantic models, datasets, dashboards, and reports, ensuring consistency and accuracy of business definitions and metrics across enterprise reporting platforms.

Requirements

Experience with enterprise data modeling tools (e.g., Erwin, SQL Data Modeler) and strong expertise in dimensional modeling methodologies including Star Schema, Snowflake Schema, Fact and Dimension design, and semantic modeling.
Bachelor’s or master’s degree in computer science, Engineering, or related field.
8+ years of experience in data engineering, with a strong emphasis on data governance and solution design.
Expertise in developing scalable data architectures for enterprise reporting.
Advanced knowledge of data governance tools and frameworks, including AWS Glue Data Catalog, to support enterprise-wide lineage, metadata, and compliance practices.
Strong understanding of cloud data platforms and services – particularly AWS (Redshift, S3, EMR, Lambda) and hybrid integrations with Azure Synapse or equivalent modern data warehouse technologies.
Proficiency in programming and scripting languages (Python, SQL, PySpark) for building testing and optimizing scalable data solutions.
Advanced experience developing Power BI semantic models, datasets, dashboards, reports, DAX measures, Power Query transformations, Row-Level Security, and performance optimization.
Excellent analytical and troubleshooting skills with attention to detail.
Strong communication skills to effectively articulate technical concepts to non-technical stakeholders.
Ability to prioritize tasks in a dynamic environment and manage multiple initiatives simultaneously.

Nice To Haves

Familiarity with MLOps and AI data pipelines leveraging cloud-native services such as AWS SageMaker, Glue ML, or Databricks for feature engineering and model deployment.
Experience with Tableau is a plus.
Certifications in cloud, database, and programming are a plus.

Responsibilities

Architect and implement scalable data pipelines to process and integrate structured and unstructured data (80%).
Design end-to-end data solutions, including Data Lake, Data Warehouse, and Data Mart, to support analytics and operational systems (80%).
Leverage UDP framework to consolidate data pipelines across healthcare domains (80%).
Support the integration of new data domains through standardized ingestion and transformation frameworks (80%).
Collaborate with stakeholders to translate business requirements into scalable, high-performing data architectures (80%).
Integrate and optimize data access across distributed systems using data federation and virtualization tools (80%).
Develop reusable data assets to support self-service analytics across programs and business domains (80%).
Design and maintain enterprise dimensional data models including fact tables, conformed dimensions, star schemas, snowflake schemas, and analytical data marts (80%).
Translate business and reporting requirements into scalable analytical data structures and semantic data models (80%).
Develop and maintain semantic layers, curated datasets, and business views to support enterprise reporting and analytics (80%).
Design, develop, and maintain Power BI semantic models, datasets, dashboards, and reports for internal and external stakeholders (80%).
Create and optimize DAX measures, calculated columns, KPIs, and business metrics to support operational and strategic reporting (80%).
Implement Power BI best practices including Row-Level Security (RLS), deployment pipelines, performance optimization, and governance standards (80%).
Partner with business users and subject matter experts to gather reporting requirements and deliver actionable analytics solutions (80%).
Ensure consistency of business definitions, metrics, and calculations across enterprise reporting and analytics platforms (80%).
Develop and enforce data governance standards, ensuring consistency, accuracy, and compliance with regulatory frameworks (e.g, HIPAA) (10%).
Implement data lineage, metadata management, and auditability practices using tools like AWS Glue Data Catalog (10%).
Establish and manage data stewardship frameworks to improve data quality and trust across the organization (10%).
Optimize system performance by designing and implementing data partitioning, indexing, and compression strategies (10%).
Ensure data security through access controls, encryption, and secure design practices (10%).