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About the Role
This position offers the opportunity to work with a US-based company, focusing on designing, building, and maintaining scalable data pipelines and cloud-based data solutions. The role emphasizes hands-on expertise with SQL, Python, Spark, Spark SQL, PySpark, and Microsoft Azure, with a strong focus on coding and end-to-end pipeline development.
Responsibilities
- Design, build, and maintain automated data pipelines that move and transform data across systems.
- Develop scalable data workflows to support analytics, reporting, and machine learning use cases.
- Build data ingestion, transformation, processing, and integration solutions within Microsoft Azure.
- Develop and maintain production-quality code using Python and SQL.
- Use Apache Spark, Spark SQL, and PySpark to process and transform large-scale datasets.
- Design data transformations that convert raw data into reliable and usable formats for downstream consumers.
- Develop efficient data integration processes across multiple data sources and destinations.
- Monitor and troubleshoot data pipelines to ensure reliability, accuracy, and performance.
- Identify and resolve data quality, pipeline, and processing issues.
- Optimize data workflows and code for performance, scalability, and maintainability.
- Collaborate with Data Analysts, Data Scientists, engineering teams, and business stakeholders to understand data requirements.
- Support the implementation and continuous improvement of cloud-based data engineering solutions.
- Document pipeline architecture, transformations, dependencies, and technical processes.
- Follow software engineering best practices for coding, testing, version control, and deployment.
Requirements
- 5–6+ years of professional Data Engineering experience.
- Proven hands-on experience designing, building, and maintaining production data pipelines.
- Strong coding and software development capabilities.
- Strong hands-on Python experience.
- Advanced SQL skills.
- Hands-on experience with Apache Spark.
- Strong experience with Spark SQL.
- Strong experience developing data solutions using PySpark.
- Experience building data solutions within Microsoft Azure.
- Experience developing automated workflows for data ingestion, transformation, and delivery.
- Experience processing and transforming large and complex datasets.
- Strong understanding of data integration, ETL/ELT, and data pipeline architecture.
- Ability to troubleshoot and optimize data pipelines and processing workloads.
- Strong understanding of data quality and validation practices.
- Strong analytical and problem-solving skills.
- Ability to work independently while collaborating effectively with cross-functional technical teams.
- US Citizenship or valid US work authorization required.
Skills
- Python
- SQL
- Spark
- Spark SQL
- PySpark
- Microsoft Azure
- Microsoft Fabric
Location
- US
Work Type
- Full-time
Experience Level
- 5-6+ years
About the Company
- SMASH believes in long-lasting relationships with talent, investing time to understand their professional goals and finding the perfect match with US clients based on technical skills and cultural fit.
- SMASH purposefully moves away from the “contractor” or “outsourcing” type of relationship.