PRIMARY FUNCTION:
The Data Engineer is responsible for implementing reliable, scalable data pipelines that support enterprise-wide data needs. This role requires expertise in data analysis, data modeling, and the design of ELT pipelines that enable efficient data integration and analytics.
The Data Engineer collaborates with data analysts, data scientists, product managers, and business stakeholders to gather requirements and deliver scalable data solutions. This role is responsible for designing, developing, and maintaining data pipelines, supporting data modeling and transformation efforts, and ensuring the reliability, quality, and performance of data assets. The Data Engineer is expected to communicate technical concepts effectively and contribute to data engineering best practices across the organization.
ESSENTIAL DUTIES AND RESPONSIBILITIES
This list may not include all of the duties that may be assigned.
- Design, build, and maintain scalable, reliable ELT/ETL pipelines using modern data engineering tools and frameworks.
- Develop and maintain data models, schemas, and data structures to support analytical and operational reporting needs.
- Create and maintain logical and physical data models following established data modeling standards and best practices.
- Collaborate with cross-functional teams to understand business requirements and deliver high-quality data solutions.
- Participate in technical discussions, solution design, and implementation activities across data engineering projects.
- Implement and maintain data quality checks, validation rules, and monitoring processes to ensure data accuracy, consistency, completeness, and reliability.
- Develop efficient, well-structured SQL queries and data transformation logic to support analytics, reporting, and operational processes.
- Monitor, troubleshoot, and optimize data pipelines for performance, reliability, and cost efficiency.
- Ensure compliance with data security, governance, and regulatory requirements across cloud and on-premise data platforms.
- Communicate technical concepts, project status, and data-related issues effectively to team members and business stakeholders.
QUALIFICATIONS
EDUCATION: Bachelor's degree in related field required. Master's degree preferred.
EXPERIENCE:
- 3-5 years of experience in data engineering or a related field required.
- Strong proficiency in SQL and experience with data transformation tools (e.g., Azure Data Factory, Apache Spark, Glue) required.
- Experience developing and maintaining ELT/ETL pipelines in cloud environments (AWS, Azure, or GCP) required.
- Good understanding of data modeling concepts, including dimensional modeling, star schema design, and normalization required.
- Hands-on experience with modern data warehousing platforms, preferably Databricks, Snowflake, or Azure Synapse required.
- Strong programming skills in Python or another modern scripting language required.
- Experience troubleshooting and optimizing data pipelines for performance, reliability, and scalability preferred.
- Familiarity with data quality, data governance, and security best practices preferred.
KNOWLEDGE, SKILLS AND ABILITIES
- Ability to understand business requirements and translate them into effective data solutions.
- Strong analytical and problem-solving skills with attention to detail.
- Effective verbal and written communication skills.
- Ability to collaborate with cross-functional teams and stakeholders.
- Proficiency in SQL, data analysis, and data transformation techniques.
- Experience working with cloud-based data platforms and modern data engineering tools.
- Knowledge of data modeling concepts, data warehousing principles, and ETL/ELT best practices.
- Familiarity with data quality, data governance, and data security practices.
- Proficient in building Power BI data models and developing dashboards and reports.
- Ability to manage multiple priorities and work effectively in a fast-paced environment.
TYPICAL WORKING CONDITIONS
- Full time remote/telework
OTHER PHYSICAL REQUIREMENTS
PERFORMANCE REQUIREMENTS
Adhere to all organizational information security policies and protect all sensitive information including but not limited to ePHI and PHI (Protected Health Information) in accordance with organizational policy, Federal, State, and local regulations.
