Analytics Engineer

Data Engineer

6-11 month contract

Remote – USA

*NYC preferred

Overview:

Our eCommerce industry client is searching for a Data Engineering Consultant with a motivated mindset and a focus on exceeding expectations. You should understand a want to build a culture of customer-first partnerships and have the desire to evolve the value of IT’s engineering organization. Working with teams with Advertising and others in Corporate Engineering, you will directly affect important company-wide functions. You will help build data warehouses, data pipelines, and promote data and analytics use across many business services such as Pricing and Yield, Sales Operations, Business Systems, Ad Operations and Sales. You will report to our Manager of Revenue Analytics within the larger Revenue Operations and Analytics team.

High Level Responsibilities:

• Data validation and maintenance across all data pipelines.
• Pipeline management using tooling from our own internal data warehouses.
• ETL management — adding new fields, data cleanup.
• Implement tooling to manage script and query deployment. Testing and deployment pipeline for queries and Glue scripts.

Top requirements:

• Data modeling, data warehousing, and building ETL pipelines
• Experience in SQL (window functions, postgresql, mysql, nosql)
• AWS Technologies (VPN setup, Redshift, Athena, S3, Glue, Code Commit)

Basic qualifications:

• 5+ years of experience as a Data Engineer or in a similar role
• At least three years of experience working in teams with collaborators to support a departmental or multi-departmental data management and analytics initiative.
• Experience with data modeling, data warehousing, and building ETL pipelines
• Python, Git, Spark, Airflow, Tableau, Docker

Preferred qualifications:

• At least 7 years of work experience in data management disciplines including data architecture, data integration, modeling, optimization, and data quality or other areas directly relevant to data engineering tasks.
• Can design and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata, and workload management.
• Experience working with and optimizing existing ETL processes and data integration and data preparation flows and moving them in production.
• Experience working with a spectrum of NoSQL, RDBMS and data stores like AWS Redshift, Athena, PostgreSQL and DynamoDB
• MWAA (Amazon’s Apache Airflow)