p>• Data pipeline development • Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data across client environments • Develop robust ETL and ELT processes to integrate data from diverse sources into modern data ecosystems, including data lakes, data warehouses, and cloud-native platforms • Implement data validation and quality checks to ensure accuracy, consistency, and reliability of data feeds • Evaluate and implement streaming and real-time data processing solutions where applicable • Data modeling and architecture • Design and maintain data models, schemas, and database structures to support analytical, operational, and AI/ML use cases • Optimize data storage and retrieval mechanisms for performance, scalability, and cost efficiency • Evaluate and implement data storage solutions, including relational databases, NoSQL databases, data lakes, data warehouses, and cloud storage services such as AWS S3, Azure Data Lake, Snowflake, and Data bricks • Contribute to the development of modern data architectures aligned with client business objectives and technical requirements • Data integration and API development • Build and maintain integrations with internal and external data sources, APIs, and enterprise systems • Implement RESTful APIs and web services for data access and consumption • Ensure compatibility and interoperability across systems and platforms • Design secure data exchange mechanisms that adhere to information security and compliance requirements • Data infrastructure management • Configure and manage data infrastructure components, including databases, data warehouses, data lakes, and distributed computing frameworks such as Spark and Data bricks • Monitor system performance, troubleshoot issues, and implement optimizations to enhance reliability and efficiency • Implement data security controls and access management policies to protect sensitive client information • Leverage cloud platforms and services, including AWS, Azure, and GCP, to deploy and manage scalable data solutions. • Proficiency in programming languages commonly used in data engineering, including Python and SQL, with Java or Scala a plus • Strong knowledge of database systems, data modeling techniques, and advanced SQL • Hands-on experience with ETL and ELT tools, such as Data bricks, Azure Data Factory, AWS Glue, Informatica, Cloud Talend, dbt, or Airflow • Experience with big data technologies and frameworks, including Spark, Hadoop, Kafka, and Kinesis • Proven experience with cloud platforms and services, such as Snowflake, AWS, and Azure, with Google Cloud Platform familiarity a plus • Strong understanding of data warehousing concepts, dimensional modeling, and modern data architecture patterns • Familiarity with GenAI applications in data engineering, agentic frameworks, and emerging technologies.