Associate Data Engineer
New
T
TaskUsData engineering
Site Location: Any TaskUs IND Site (Remote); Work Setup: Work from HomeFull-TimeJunior
Salary not disclosed
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Job Details
- Experience
- At least 2 years of experience in Data Engineering; at least 2 years of data modeling; at least 2 years of hands-on development experience using ETL tools.
- Required Skills
- PythonETLApache KafkaData modeling
Requirements
- Have at least 2 years of experience in data engineering.
- Have at least 2 years of data modeling experience.
- Have at least 2 years of hands-on ETL tool development experience.
- Know Python, Apache Kafka, AWS Redshift, AWS Glue, AWS S3, and Pentaho Data Integration.
- Understand data warehousing, traditional and MPP database designs, and star and snowflake schemas.
- Know MPP database architecture, design, and implementation, such as Teradata, Snowflake, or Redshift.
- Have 3 years of development experience using cloud-based analytics solutions; AWS or GCP is preferred.
- Know how to design and implement streaming pipelines using Apache Kafka, Apache Spark, or Segment.
- Know database tuning and ETL tuning.
- Have a bachelor's/college degree in Computer Science/Information Technology or Computer/Telecommunication Engineering, or 4–8 years of experience in place of a degree.
Responsibilities
- Configure solutions to move data from operational and external environments into the business intelligence environment.
- Manage data integration and ETL jobs, including consolidation, aggregation, filtering, validation, data quality routines, and dimension creation.
- Configure and deploy automated jobs using pre-approved, reusable patterns, accounting for data dependencies, schedules, validation routines, and data stream updates.
- Design, develop, verify, and optimize data models according to business requirements.
- Collaborate with technical users to define data requirements and establish business rules.
- Collaborate with business stakeholders to deliver and enhance the BI platform and datasets.
- Perform unit and system integration testing, peer reviews, and maintain current documentation.
- Monitor error handling, logging, and performance; troubleshoot configuration and data errors.
- Identify and implement ETL environment optimizations and monitoring, quality, and validation processes.
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