Senior Data Engineer

Ebara Elliott Energy•Jeannette, PA

About The Position

The Data Engineer builds robust, complex data pipelines to move data from various source systems into the Enterprise Data Lakehouse. The role architects, troubleshoots, and designs complex workflows and transformations, and establishes reliable data integration across cloud and on-premises environments. It works with internal business users across Operations, Sales, Marketing, and Finance at global locations, and collaborates with visualization engineers, data analysts, IT, and data scientists to onboard new data sources.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Science, or Analytics.
At least 10 years of hands-on experience as an ETL engineer.
At least 3 years of experience with Databricks.
Experience with database systems such as Oracle and SQL Server.
Experience in cloud environments, preferably AWS.
Experience with SQL, data modeling, and Informatica, Fivetran, or Azure Data Factory.
Deep understanding of extracting data from ODBC sources and using APIs.
Basic Python skills.
Ability to work with applications, business units, data analysts, visualization engineers, and data scientists.
Sophisticated skills in understanding, influencing, driving, and negotiating with internal customers.
Ability to use initiative and extensive knowledge to resolve complex issues and develop novel solutions.
Ability to work in a fast-paced, changing environment and independently provide consultative technical advice.

Responsibilities

Review source-to-target mapping documents.
Design and document ETL pipelines using industry-standard ETL tools.
Build and streamline robust data pipelines with clear visibility into data lineage.
Monitor data flows across databases, cloud environments, and on-premises systems.
Implement best practices and standardize data integration across sources.
Profile data, detect anomalies, and develop solutions to improve data quality.
Build and implement Master Data Management processes, including defining algorithms and writing code to cleanse data.
Establish robust and efficient data flows with logging and effective troubleshooting methods.
Work independently and provide consultative advice on technical issues.
Resolve complex application and data integration issues, including developing new solutions where needed.
Interact regularly with internal business users across departments and global locations.
Collaborate with visualization engineers, data analysts, IT, and data scientists to enable new data sources in the pipeline.