Data Engineer

Design, develop, and maintain data solutions for data generation, collection, and processing   

Be a key team member that assists in design and development of the data pipeline  

Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems  

Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions  

Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks  

Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs  

Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency  

Implement data security and privacy measures to protect sensitive data  

Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions  

Collaborate and communicate effectively with product teams  

Collaborate with Data Architects, Business SMEs, and Data Scientists to design and develop end-to-end data pipelines to meet fast-paced business needs across geographic regions  

Identify and resolve complex data-related challenges  

Adhere to best practices for coding, testing, and designing reusable code/component  

Explore new tools and technologies that will help to improve ETL platform performance  

Participate in sprint planning meetings and provide estimations on technical implementation  

Design and develop data pipelines leveraging Databricks, PySpark, and SQL to ingest, transform, and process large-scale datasets. 

Engineer solutions for both structured and unstructured data to enable advanced analytics and insights. 

Implement automated workflows for data ingestion, transformation, and deployment using Databricks Jobs and notebooks, with ongoing monitoring and scheduling. 

Apply performance optimization techniques, including Spark job tuning, caching, partitioning, and indexing, to improve scalability and efficiency. 

Build integrations with multiple data sources, such as SQL databases, APIs, and cloud storage platforms, ensuring seamless connectivity and reliability. 

• Collaborate effectively with global teams across time zones to maintain alignment, resolve issues, and deliver on shared objectives.