Senior Software Engineer - Python(Data Engineering)

In this role, you will: Lead moderately complex initiatives and deliverables within technical domain environments Contribute to large scale planning of strategies Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments Review moderately complex technical challenges that require an in-depth evaluation of technologies and procedures Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements Collaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals Lead projects and act as an escalation point, provide guidance and direction to less experienced staff Required Qualifications: 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: Technical Experience (4+ years) Hands-on engineering experience using: Python, Spark, Iceberg, Hive Dremio or virtualization/federation/semantic layer tools Any major cloud Azure or GCP ETL tools: Ab Initio (preferred), Informatica, DataStage Databases: Oracle, MS SQL, Teradata Orchestration: Autosys, Airflow (preferred) Strong SQL development and tuning skills Proven experience supporting and optimizing large enterprise-scale data environments. Working with modern Data Warehousing, Data Lakes, and Lakehouse architectures. Monitor, tune, and troubleshoot large-scale distributed systems. Diagnose memory, performance, partitioning, and query-related inefficiencies across compute engines. Ensure system reliability, scalability, and high throughput in enterprise data environments. Expertise in designing / implementing modern data platforms with: Standardization of patterns, frameworks, and reusable components Automated code-generation, CI/CD and DevOps best practices Cloud-native and open-table formats (e.g., Iceberg) Hands-on experience in GenAI, Agentic AI, and LLM adoption, such as: Building RAG architectures, vector stores, embedding pipelines Integrating data systems with enterprise AI platforms Using LLMs to improve data quality, metadata extraction, or automation of data engineering tasks Familiarity with data governance and security frameworks, including privacy, compliance, lineage, and access control. Strong communication skills and collaborate with cross-technical / functional teams. Ability to work in an agile, fast-paced environment while delivering high-quality results. Passion for innovation, continuous learning, and adopting modern practices including AI-driven development. .