Data Engineer

About the Role

As a Lead Data Engineer, this professional will be a key member of the Data team, leading and mentoring 3–5 Data Engineers while remaining hands-on in the design, development, and optimization of enterprise data solutions.

The role focuses heavily on data sourcing, ETL development, data warehousing, data lakes, and Dataverse environments, with strong emphasis on IBM DB2, Informatica PowerCenter, SQL/PLSQL, data modeling, and performance tuning. The ideal candidate should be comfortable working with large and complex datasets, translating business requirements into technical solutions, and collaborating closely with data providers, analysts, developers, and business stakeholders.

Key Responsibilities

• Lead, mentor, and provide technical guidance to a team of 3–5 Data Engineers.
• Design, develop, and maintain complex ETL processes using Informatica PowerCenter.
• Develop stored procedures, triggers, MQTs, views, and data workflows within an IBM DB2-based Data Warehouse.
• Perform data profiling and source-system analysis to ensure data can be accurately integrated into enterprise data models.
• Monitor and optimize queries, tables, stored procedures, and data loads to improve performance and scalability.
• Design and maintain data models, schemas, and structures for efficient reporting and analytics.
• Support QA and UAT, troubleshooting data issues, identifying root causes, and defining appropriate solutions.
• Work closely with Data Analysts, Data Scientists, data providers, and business teams to understand requirements and deliver reliable data solutions.
• Ensure data pipelines maintain high levels of quality, integrity, security, and compliance.
• Document data workflows, technical processes, and best practices.

Required Qualifications

• 5+ years of experience in data development, data engineering, or data solutions within complex, high-volume environments.
• 5+ years of experience developing complex ETLs with Informatica PowerCenter.
• 5+ years of SQL/PLSQL experience, including complex and ad-hoc queries for data analysis.
• 5+ years of experience with IBM DB2, including stored procedures, triggers, MQTs, and views; DB2 v10.5 is a plus.
• Strong experience with DB2 performance tuning, including tables, queries, stored procedures, and data loads.
• Strong understanding of data warehouse architecture and advanced data warehousing concepts, including factless fact tables and temporal/bi-temporal models.
• Experience with conceptual, logical, and physical E-R data models.
• Experience with scripting languages such as Python and/or Perl.
• Strong understanding of ETL, data integration, data modeling, data quality, and data governance.
• Ability to translate complex business requirements into technical designs and scalable data solutions.
• Experience working with both Agile and Waterfall methodologies.
• Strong communication and stakeholder management skills.
• Ability to manage multiple concurrent projects and shifting priorities with minimal supervision.
• Bachelor's degree in Computer Science, Information Technology, or a related field.

Preferred Qualifications

• Experience with Azure Cloud and Azure-based data services.
• Experience with Azure SQL Data Warehouse.
• Familiarity with Azure Data Factory, AWS Glue, Apache Kafka, or similar data integration technologies.
• Experience working with Data Warehouse, Data Lake, and Dataverse environments.
• Knowledge of data governance, data quality, security, and compliance practices.
• Familiarity with Power BI, Tableau, Looker, or other BI platforms.
• Previous experience leading or mentoring Data Engineering teams.

Ideal Candidate

The ideal candidate is a hands-on Lead Data Engineer with deep expertise in IBM DB2, Informatica PowerCenter, SQL/PLSQL, ETL development, and enterprise data warehousing. They should combine strong technical depth with the ability to lead a small engineering team, analyze complex data problems, understand business requirements, and independently drive solutions from requirements through implementation and UAT.

This is particularly suited for someone who enjoys working in complex data environments with large volumes of data, while also acting as a technical reference for engineers and business stakeholders.