Sr Analyst, Data Engineer

Scotts Miracle-Gro•Marysville, OH

3d•$111,500 - $131,200•Remote

About The Position

The Senior Analyst in Data Engineering is a technical role that supports data processing and integration efforts. You'll use your advanced analytical skills to improve data pipelines and ensure data delivery is consistent across projects. This role bridges the gap between data engineers and data analysts/scientists to optimize data-driven initiatives.

Requirements

Bachelors Degree in Computer Science in Engineering, mathematics, or related technical field
Databricks, python, Spark SQL, Airflow
4-7 years of experience preferred
6+ years of experience in Data Engineering
Strong communication skills to timely and effectively convey information and consult with others
6+ years of developing and maintaining data pipelines (ETL/ELT)
6+ years of programming experience in Databricks, Python, and Apache Spark
Experience with Apache Airflow
Experience in deploying solution in AWS Cloud with emphasis on Amazon S3, Glue, Athena, EMR, RDS, and lake formation
Experience in data modelling and writing complex SQL queries

Nice To Haves

AWS Certified Data Engineer

Responsibilities

Perform data extraction, transformation, and loading (ETL) processes.
Design and implement complex data transformation logic to meet business requirements.
Ensure the accuracy and reliability of data processing tasks.
Implement and execute data quality checks to identify and address anomalies.
Collaborate with stakeholders to define and enforce data quality standards.
Monitor and troubleshoot data quality issues to maintain high standards.
Manage and optimize databases to support efficient data storage and retrieval.
Implement database schemas, indexes, and structures for optimal performance.
Collaborate with database administrators to ensure database integrity and security.
Support data analysts and data scientists in creating reports and analytics solutions.
Collaborate with business intelligence teams to integrate data for reporting purposes.
Ensure data availability and accuracy for analytical purposes.
Work closely with data scientists to understand their data requirements and provide support.
Assist in the integration of machine learning models into data engineering pipelines.
Optimize data processing for machine learning tasks.
Identify opportunities for process automation within data engineering workflows.
Implement automation scripts and tools to streamline repetitive tasks.
Enhance operational efficiency through the use of automation.
Document data engineering processes, workflows, and best practices.
Share knowledge and provide training to team members and other stakeholders.
Maintain up-to-date documentation for reference and training purposes.