The Senior Data / AI Engineer is responsible for designing, building, and maintaining enterprise-scale data and analytics solutions using modern cloud-based architectures. This role focuses on developing scalable data integration frameworks, distributed data systems, and analytics pipelines that support advanced reporting, machine learning, and data-driven decision-making across the organization.
This position works closely with data, analytics, and engineering teams to implement best practices in data management, data quality, and security while enabling real-time and batch data processing use cases.
Design and implement scalable data warehouse and analytics solutions using modern cloud data platforms
Build and maintain data integration pipelines between transactional systems and analytics environments
Develop solutions to ingest, transform, and enrich data from internal and external sources
Support multiple data patterns including real-time streaming, batch processing, BI, advanced analytics, and machine learning
Develop data products that incorporate enrichment, feature engineering, and ML-ready datasets
Collaborate with cross-functional teams and contribute to shared technical knowledge and standards
Apply data management principles to ensure reliability, performance, and data quality
Follow security best practices related to access control, encryption, and network protection
Bachelor’s degree in Computer Science or a related technical field
5+ years of experience building data-driven or analytics solutions
3+ years of experience working with cloud platforms (AWS or equivalent)
Strong experience with Python and SQL for data engineering and analytics
Hands-on experience with cloud-based data warehouses (e.g., Snowflake, Redshift, Databricks)
Experience designing and building data pipelines for structured, semi-structured, and unstructured data
Familiarity with ETL/ELT tools and data integration frameworks
Strong understanding of data security, governance, and best practices
Experience with real-time or streaming data architectures
Familiarity with messaging or event-driven platforms (e.g., Kafka or similar)
Knowledge of cloud-native services for compute, storage, networking, and identity management
Experience with serverless technologies and infrastructure-as-code concepts
Exposure to machine learning frameworks and libraries
Understanding of generative AI concepts such as prompt engineering, retrieval-augmented generation (RAG), or model tuning
Experience supporting ML model development or deployment pipelines
Strong written and verbal communication skills
Ability to work independently while managing multiple priorities
Demonstrated attention to data quality, accuracy, and governance
Proven ability to meet deadlines and deliver high-quality solutions
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