Senior Data Engineer II

Sr Data Engineer II oversees all technical aspects of data architecture and database management, ensuring optimal performance and security, while leading teams and collaborating with architects and product teams to design and implement effective data solutions, including advanced Big Data management strategies through partitioning and indexing.
Education Qualifications:
Bachelor’s degree in Computer Science, Computer Engineering, and/or comparable experience; advanced degree preferred
Knowledge of Big Data / NoSQL design and development with variety of data stores (document, column family, graph, etc.)
Knowledge of database management system products and ecosystem – e.g., storage formats, access algorithms, data management processes, administration, data maintenance, replication, high availability, encryption, etc.”
Knowledge of distributed (multi-tiered) systems, algorithms, and relational & non-relational databases
Knowledge of XML/JSON and schema development/reuse, relational database management systems, Open Source
Work Experience:
Data engineering and development experience or a related job
Experience in SQL and Python (or any other programming language like Java)
Experience in design and development across one or more database management systems (e.g., HBase, Hive, GCP tools, NoSQL, BigTable, BigQuery, Spanner, IMS, DB2 zOS, Oracle, MS SQL Server, PostgreSQL, Netezza (PDA/DB2AA) as appropriate
Experience in NoSQL databases (e.g., MongoDB, Couchbase, Cassandra, and Redis)
Experience in data modeling tools (e.g., ErStudio, ErWin)
Experience in performance tuning in the Big Data space
Experience in distributed (multi-tiered) systems, algorithms, and application development
Experience in database development and support of online transaction processing and/or online analytical processing systems
Experience in software delivery methodology (Agile, Scrum, Agile XP, Test-Driven Development, Waterfall, etc.)
Experience in infrastructure design for performance (e.g., network-attached or local-attached storage integration - such as Nutanix)
Experience designing and implementing ETL/ELT pipelines and large-scale data-processing solutions.
Experience with performance tuning, resiliency, high availability, data replication, and production troubleshooting.
Experience with software-engineering practices including Git, automated testing, CI/CD, code reviews, and production support.
Ability to collaborate effectively with architects, engineers, product teams, and business stakeholders.
Experience mentoring engineers and providing technical leadership on complex initiatives.
AI / GenAI Experience – Preferred
Working knowledge of Generative AI and LLM-based application architectures.
Exposure to RAG, embeddings, vector databases, semantic/hybrid search, and knowledge retrieval.
Experience integrating LLM APIs or frameworks such as LangChain/LangGraph is advantageous.
Basic Understanding of AI evaluation, grounding, guardrails, observability, security, and responsible AI concepts.
Experience building data pipelines for unstructured/semi-structured content and preparing enterprise data for AI consumption.
Ability to rapidly prototype AI/GenAI use cases using Python and transition viable POCs toward production.
Supervises and mentors Data Engineers and cross-functional team of colleagues in specific tasks or projects, fostering their professional growth and ensuring adherence to best practices
Collaborates with delivery architects and engineers to design optional data tier solutions to meet enterprise platform objectives and goals
Collaborates with product teams to prioritize features for ongoing sprints, designs data tier solutions with architects and engineers, and contributes to decisions about tools and methods
Mentors teams of junior and senior Data Engineers through ongoing development efforts
Manages all technical aspects of data architecture and database management, including developing prototypes, new database structures, and application programming interfaces
Transforms logical data architectures into physical designs, provides database administration services, and continuously improves database design and performance
Debugs database components, fixes performance defects, and ensures optimal data replication, high availability, backup, and recovery processes
Leads the development of plans, strategies, best practices, and standards for data management, security, and processes
Oversees and develops Big Data management strategies by designing and implementing advanced partitioning and indexing solutions
Collaborates and co-creates effectively with teams in product and the business to align technology initiatives with business objectives

Work arrangement
Hybrid