PRIMARY FUNCTION

The Senior Data Engineer is responsible for architecting and implementing reliable, scalable ELT/ETL workflows that support enterprise-wide data needs. This role requires deep expertise in data analysis, data modeling, and the design of complex ELT pipelines that enable efficient data integration and analytics. The Senior Data Engineer collaborates closely with cross-functional teams—including data analysts, data scientists, product managers, and business stakeholders—to define requirements, drive technical discussions, and develop high-quality data solutions. Additionally, this role is expected to clearly communicate technical concepts, translate complex data challenges into actionable business insights, and guide teams toward best-practice data engineering approaches.

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. Architect and optimize data models, schemas, and data platforms to support both analytical and operational workloads. Design, document, and maintain enterprise-level data models (conceptual, logical, and physical) using data modeling best practices. Collaborate with cross-functional teams to understand requirements, define data strategies, and deliver high-quality engineering solutions. Lead and drive technical discussions, solution design, and architectural decisions across teams. Implement and enforce comprehensive data quality standards, validation rules, and monitoring frameworks to ensure accuracy, consistency, completeness, and reliability across all data assets. Develop efficient, well-structured SQL and complex data transformation logic to support analytics and reporting. Monitor, troubleshoot, and enhance data pipelines for performance, reliability, and cost efficiency. Ensure data security, compliance, and adherence to best practices across cloud and on-premise data systems. Communicate complex technical topics in a clear, business-aligned manner to stakeholders at all levels. Mentor and guide junior and mid-level engineers, contributing to team growth and engineering excellence.

QUALIFICATIONS EDUCATION

Bachelor's degree in related field required. Master's degree preferred.

EXPERIENCE

10+ years of experience in data engineering or a related field required. Expert-level proficiency in SQL and experience with data transformation tools (e.g., Azure Data Factory, Apache Spark, Glue) required. Proven experience architecting and maintaining large-scale ELT/ETL pipelines in cloud environments (AWS, Azure, or GCP) required. Deep understanding of data modeling concepts (dimensional modeling, star schema, normalization) required. Hands-on experience with modern data warehousing platforms, preferably Databricks/Snowflake/Azure Synapse required. Experience in DevOps activities, including implementing CI/CD pipelines and workflow automation for data engineering and analytics platforms required. Strong programming skills in Python or another modern scripting language required.

KNOWLEDGE, SKILLS AND ABILITIES

Demonstrated ability to translate complex business requirements into scalable, high-impact technical solutions. Excellent communication, documentation, and stakeholder engagement skills. Proficient in building Power BI data models and creating effective dashboards. Demonstrated ability to translate complex business requirements into scalable, high-impact technical solutions. Excellent communication, documentation, and stakeholder engagement skills. Proficient in building Power BI data models and creating effective dashboards.

TYPICAL WORKING CONDITIONS

Full time remote/telework

OTHER PHYSICAL REQUIREMENTS

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