Sr. Data Engineer with AIML

Sr Engineer Data designs and delivers scalable data architectures and advanced data engineering solutions across on-premises cloud and hybrid platforms to support enterprise analytics and customer intelligence use cases. This role partners closely with data engineers architects and analytics teams to analyze design and optimize data warehouse and analytics solutions that power personalization and data-driven decision-making within the customer data platform. The Sr Engineer provides hands-on technical leadership mentors engineers and drives the evolution of modern data architectures. Success is measured by solution effectiveness scalability performance and growth in team capability and architectural maturity.

What You ll Do
Design and develop advanced data engineering solutions that enable data pipelines visualization and analytical tools
Architect and implement scalable data platforms across on-premises cloud and hybrid environments
Build optimize and operate complex batch and real-time and near real-time streaming data pipelines
Build optimize and operate low-latency sub-second APIs on highly scalable fault-tolerant architectures.
Design and maintain high-throughput data pipelines to support agentic AI workloads and real-time multi-agent MCP orchestration.
Build and maintain enterprise-scale Graph Databases that power organizational knowledge graphs and connected data experiences.
Perform data wrangling exploration and discovery across heterogeneous data sources to generate insights
Lead development of large-scale data processing solutions using Databricks and Spark technologies
Contribute to team knowledge sharing and advancement of data engineering standards and capabilities
Mentor engineers to strengthen technical skills delivery quality and professional growth
Support project definition activities including estimation planning and scoping in partnership with management What You ll Bring
Bachelor s Degree in Computer Science Computer Engineering or a related field or equivalent practical experience
8 years of hands-on experience designing building and supporting data engineering solutions
Strong experience developing and migrating data solutions across cloud platforms
Demonstrated technical leadership and mentoring experience
Strong analytical and problem-solving skills applied to complex data challenges
Ability to manage multiple concurrent initiatives with strong organizational and prioritization
Passion for learning and applying new data and platform technologies

Must Have Skills
Core Data Engineering & Platforms
Advanced experience designing and building complex data pipelines using Python and SQL
Strong experience with cloud platforms and services including Azure (Data Factory Data Lake Event Hub Functions Web Apps Cosmos DB)
Databricks with PySpark Spark SQL Scala Spark including cluster configuration autoscaling and performance optimization (e.g. Photon)
Snowflake data warehousing including schema design (star/snowflake) query optimization and cost/performance tuning
Expertise in SQL NoSQL and relational database design and development
APIs Streaming & Integration
Proficiency designing and consuming RESTful APIs with secure authentication and authorization (OAuth 2.0 JWT API keys)
Experience using Postman and Swagger/OpenAPI for API testing documentation and validation
Working knowledge of message queuing stream processing and highly scalable big-data data stores
Engineering Practices
Advanced knowledge of data pipeline development using Python and experience with languages such as SQL DAX Java Scala and/or Go
Experience performing root-cause analysis and using technology to solve complex business problems
Experience adopting AI-assisted development tools and building AI-powered agents or agentic workflows to automate engineering tasks accelerate delivery and enhance data platform capabilities
Experience with NL-to-SQL query generation using Databricks Genie and Snowflake Cortex to enable natural language access to data assets
Hands-on experience with Unity Catalog for data governance fine-grained access control and end-to-end data lineage
Experience developing AI-assisted dbt models including AI-generated transformation logic automated documentation and intelligent testing
Familiarity with AI-powered data quality tools for automated profiling anomaly detection and pipeline observability Nice to Have
Experience with Iceberg table design and optimization
Experience with Unity Catalog governance and security
Cloud platform certifications such as Azure Certified Solutions Architect Azure Cloud Practitioner or MCSA
Experience contributing to enterprise data governance security or compliance initiatives
Experience with ML feature platforms (e.g. Feast Tecton or Databricks Feature Store) and vector database engineering (e.g. Pinecone Weaviate pgvector)
Experience designing and operating streaming AI pipelines and applying MLOps practices to data engineering workflows
Exposure to end-to-end ML pipeline ownership and real-time AI inference plumbing in production environments