Senior Specialist - Data Engineering

Role overview

Strategic and operational responsibilities

• Support data strategy and internal processes; promote self-service analytics and help business users upskill.
• Recommend ways to organize, analyze, and present data; evaluate existing systems and support analytics and AI roadmap planning.
• Build a data community and lead business teams in using data as a strategic asset.
• Design, develop, tune, and monitor scalable pipelines; implement data quality checks and validation.
• Design and maintain warehouses, lakes, storage systems, processing frameworks, and data models.
• Manage the analytics portfolio, gather business requirements, and translate needs into technical specifications and data-store designs.
• Administer data warehouses and BI tools; transition BI systems to Operations and Support and support enterprise environment availability and performance.

People and process responsibilities

• Provide technical assistance and cross-training; develop team competencies and mentor junior team members.
• Motivate team members, support service delivery, and encourage professional development.
• Implement configuration management and change controls; establish technology best practices, guidelines, and repeatable processes.
• Communicate with internal, external, and customer teams in project environments.

Education and experience

• Minimum bachelor's or master's degree in computer science.
• Minimum 8 to 11 years of experience implementing advanced analytics, data engineering, and reporting solutions.
• Data warehousing, ETL, analytics, and reporting certifications are preferred.

Knowledge and skills

• Requirements analysis; hands-on Python for data engineering; Airflow; multiple data sources and APIs.
• Algorithms and basic statistics; SQL and RDBMS such as DB2, Oracle, or SQL Server.
• Data wrangling, visualization, analytics, and reporting; Alteryx and BI applications such as Power BI, Tableau, or Business Objects.
• Query languages include PL/SQL, SQL Server, Spark SQL, and T-SQL.
• Data modeling includes Star/Snowflake schemas, data marts, relational and dimensional modeling, fact and dimension tables, and physical and logical models.
• Cloud data warehouse knowledge on AWS, Azure, or Google platforms.