Your responsibilities will include performance tuning and optimization of existing solutions, building and maintaining ETL pipelines, as well as testing and documenting current data flows
You will also be involved in implementing tools and processes to support data-related projects and promoting the best development standards across the team
Design, build, test and deploy Cloud and on-premise data models and transformations in Cloud Native or dedicated toolset
Optimize data views for specific visualization use cases making use of schema design partitions, indexes, down-sampling, archiving, etc. to manage trade-offs such as performance and flexibility
Review and refine, interpret and implement business and technical requirements
Ensure you are part of the on-going productivity and priorities by refining User Stories, Epics and Backlogs in Jira
Onboarding new data sources, design, build, test and deploy Cloud data ingest, pipelines, warehouse and data models/products

Requirements

At least 2-3 years of commercial experience as a Data Engineer
Strong Python and PySpark skills
Strong hands-on experience with SQL and query optimization
Experience with ETL/ELT pipelines development, testing, and management
Strong experience with Hadoop
Understanding of key concepts around Data Warehousing, Data Lakes and Data Lakehouses
Experience with Cloud Data engineering toolset, preferably GCP
Nice to have: Experience with Java/Scala

Benefits

Competitive salary
B2B contract

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

PythonPySparkSQLETLELTHadoopCloud Data engineeringJavaScalaData Warehousing

Soft skills

performance tuningoptimizationtestingdocumentationbusiness requirements interpretationtechnical requirements implementationteam collaborationprocess implementationproductivity managementuser story refinement