Data Engineer
Limetaya Technology LLC
United States
19 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Compensation
$96,000.0 - $131,000.0
Working hours
Regular working hours
Job source
Tech stack
Application Programming Interfaces (APIs)
Airflow
Amazon Web Services
Data Analysis
Automation of Tests
Microsoft Azure
BigQuery
Cloud Computing
Code Review
Databases
Continuous Integration
Data Validation
+36 more
Information Engineering
Data Integration
Extract Transform Load (ETL)
Data Transformation
Data Systems
Data Warehousing
Relational Databases
Database Queries
DevOps
Programming Tools
Document-Oriented Databases
Github
Revision Control Systems
Python (Programming Language)
Power BI
Software Tools
Standard Sql
Software Engineering
SQL Databases
Tableau (Software)
Workflow Management Systems
Data Processing
Scripting
Google Cloud
Data Storage Technologies
Cloud Platform System
Snowflake
Apache Spark
Git
Microsoft Fabric
Information Technology
Data Analytics
Software Version Control
Data Pipelines
Databricks
Programming Languages
Job description
You will collaborate with experienced data engineers, analysts, and other technical and business stakeholders to understand data requirements, develop efficient data processing solutions, and maintain data quality across systems. This position offers an opportunity to contribute to real-world data engineering projects while strengthening your skills in cloud-based data platforms, data warehousing, automation, and modern engineering practices., * Develop, test, and maintain data pipelines to collect, transform, and integrate data from databases, APIs, files, and other sources.
- Write and optimize SQL queries and use Python to support data processing, transformation, validation, and automation.
- Assist in building and maintaining ETL/ELT workflows that deliver accurate, consistent, and analytics-ready datasets.
- Work with cloud-based data platforms and data warehouses to support data storage, processing, and integration requirements.
- Monitor data pipelines, investigate data discrepancies, troubleshoot processing issues, and support improvements in reliability and performance.
- Contribute to data modeling and transformation tasks that support reporting, business intelligence, and analytical applications.
- Apply data quality checks and validation practices to help maintain data accuracy, completeness, and consistency.
- Use Git-based version control and participate in code reviews, testing, and established development workflows.
- Assist with deploying data pipeline updates through CI/CD processes and help document data flows, transformations, and technical procedures.
- Collaborate with data engineers, analysts, and business stakeholders to understand requirements and deliver practical data solutions.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.
- 2+ years of experience in data engineering, data analytics, or a related technical role.
- Working knowledge of SQL, including writing queries, joining tables, filtering data, and working with relational databases.
- Experience with Python or a similar programming language for data manipulation, scripting, or automation.
- Understanding of data engineering fundamentals, including data pipelines, data integration, and ETL/ELT processes.
- Familiarity with relational databases, data warehousing concepts, and basic data modeling techniques.
- Exposure to a cloud platform such as AWS, Microsoft Azure, or Google Cloud Platform.
- Familiarity with Git, GitHub, or similar version control tools and an understanding of software development practices.
- Strong analytical, problem-solving, and troubleshooting skills, with attention to data quality and detail.
- Good written and verbal communication skills and the ability to collaborate with technical and non-technical team members., * Exposure to data engineering platforms or tools such as Databricks, Apache Spark, Snowflake, BigQuery, or Microsoft Fabric.
- Familiarity with data transformation and orchestration tools such as dbt, Apache Airflow, or similar technologies.
- Basic understanding of CI/CD, automated testing, monitoring, and data pipeline scheduling.
- Experience with business intelligence tools such as Power BI or Tableau.
- Interest in learning new data technologies and applying modern development tools to improve productivity.
Benefits & conditions
- 401(k)
- Dental insurance
- Health insurance
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