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.