Data Engineer
Required skills
Bachelor’s or Master’s degree in Computer Science, Statistics, Analytics, Data Engineering, or a related field, 3–5 years of cloud data engineering experience on a major cloud platform, preferably AWS, Hands-on experience building data pipelines in cloud environments, Demonstrable experience with PySpark, Hands-on experience with Databricks, Strong proficiency in SQL and experience working with SQL Server or similar relational databases, Hands-on knowledge of data modeling, data warehousing, and BI reporting concepts, Experience designing data models that support analytics, reporting, and business intelligence use cases, Familiarity with BI tools and reporting environments such as Power BI or Tableau, Experience maintaining, troubleshooting, and optimizing ETL/data pipeline processes, Experience documenting technical processes, data infrastructure, and pipeline logic
Preferred skills
Experience with streaming data tools such as Kafka, Kinesis, or similar technologies
Technologies
SQL Server, AWS, cloud-based data platforms, PySpark, Databricks, SQL, cloud data services, data modeling, data warehousing, BI reporting, Power BI, Tableau, Kafka, Kinesis
Responsibilities
Design, build, and maintain scalable cloud-based data pipelines and ETL processes to support BI and reporting solutions, Develop and maintain data infrastructure across SQL Server, AWS, and cloud-based data platforms, Build, troubleshoot, and optimize data pipelines using PySpark, Databricks, SQL, and cloud data services, Design and implement data models optimized for analytics, warehousing, and reporting use cases, Partner with BI Analysts to manage client reporting requirements and support data-driven insights, Automate manual data processes to improve efficiency, consistency, and data quality, Maintain and improve existing data infrastructure, pipelines, and reporting data flows, Work with the product Technical Writer to document data infrastructure, pipelines, models, and processes, Collaborate with Product, BI, Engineering, and Data Analytics teams to support reliable reporting and analytics solutions, Share technical knowledge with coworkers and contribute to continuous improvement of data systems and processes, Stay current with modern data engineering technologies, including Databricks, PySpark, cloud platforms, data modeling, and streaming data tools, Participate in client-facing sessions when needed, including design discussions, technical presentations, and training
Seniority
Not specified
Domain
Data engineering, cloud computing, data analytics, business intelligence
