Data Engineer

Career Category Engineering Job Description Role Description: The role is responsible for designing, building, maintaining, analyzing, and interpreting data to provide actionable insights that drive business decisions. This role involves working with large datasets, developing reports, supporting and executing data governance initiatives and visualizing data to ensure data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture and ETL processes Roles & Responsibilities: 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. Basic Qualifications and Experience: Bachelor’s / Master’s degree and 4 to 8 years of Computer Science, IT or related field experience Functional Skills: Must-Have Skills Hands-on experience with big data technologies and platforms, such as Databricks, Apache Spark (PySpark, SparkSQL), workflow orchestration, performance tuning on big data processing Proficiency in data analysis tools (e.g. SQL) and experience with data visualization tools Excellent problem-solving skills and the ability to work with large, complex datasets Strong understanding of data governance frameworks, tools, and best practices. Good-to-Have Skills: Knowledge of data protection regulations and compliance requirements (e.g., GDPR, CCPA) processing Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model development Strong understanding of data modeling, data warehousing, and data integration concepts Knowledge of Python/R, Databricks, SageMaker, cloud data platforms Experience implementing automated orchestration and monitoring of data pipelines using Databricks Jobs, Apache Airflow, or similar workflow tools. Familiarity with performance optimization techniques for big data processing, such as Spark job tuning, caching, partitioning, and indexing. Exposure to multi-source integration involving APIs, SQL databases, and cloud storage platforms. Demonstrated ability to collaborate across global teams and time zones, ensuring alignment and delivery in distributed environments. Professional Certifications (Preferred): Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments) Soft Skills: Excellent critical-thinking and problem-solving skills Strong communication and collaboration skills Demonstrated awareness of how to function in a team setting Demonstrated presentation skills .