Ascendion - Senior Data Engineer

Description :

- Design, build, and optimize scalable data engineering solutions in Azure/GCP cloud environments.

- Develop secure and reliable ETL/ELT pipelines using Python, PySpark, SQL, and UNIX/Linux scripting.

- Apply expertise in query optimization, data structures, transformations, metadata management, dependency tracking, and workload orchestration.

- Automate workflows using orchestration and scheduling tools.

Cloud & DevOps Enablement :

Work with cloud-native services including :

- Kubernetes, containerized services, cluster management, cloud storage, and workspace management.

- Build and maintain CI/CD pipelines following DevOps best practices.

- Collaborate effectively using Git-based version control in multi-developer environments.

AI/ML & Agentic Systems Support :

Support and enhance AI/ML initiatives including :

- Feature engineering, model deployment, and MLOps platform integrations.

- Work with AI/ML frameworks such as TensorFlow, PyTorch, and libraries like scikit-learn, MLflow.

- Build solutions using multi-agent AI tech stacks, including :

i. PydanticAI

ii. LangChain

iii. LangGraph

- Exposure to Agent-to-Agent Protocol or Model Context Protocol (MCP) is highly desirable.

- Cross-functional Collaboration & Agile Delivery

- Partner with data scientists, ML engineers, architects, and business teams.

- Translate complex technical concepts for non-technical stakeholders.

- Participate in Agile ceremonies within Scrum or Kanban frameworks.

Service & Operational Excellence :

- Track and manage work effectively using the ServiceNow ticketing system.

- Ensure performance, scalability, and operational excellence across data and AI systems.

Required Skills & Qualifications :

- 5 to 9 years of experience in data engineering or related roles.

- Strong hands-on programming in Python, PySpark, SQL, and UNIX/Linux scripting.

- Proven experience designing and deploying data solutions in Azure or GCP (cloud certification preferred).

- Strong understanding of query tuning, distributed computing, data modeling, and data lifecycle management.

Familiarity with :

- CI/CD pipelines and DevOps tools

- Containerization (Docker), Kubernetes

- Cloud-native compute and storage services

- Excellent communication and documentation skills.

- Experience with automation and orchestration tools (Airflow, Databricks, Prefect, etc.).

- Good understanding of modern API and microservice architectures.

- Knowledge of Agile methodologies (Scrum, Kanban).