Experience: 5+ years of relevant experience

WHY I DO MY JOB? | Job Purpose.

Design, build, and operate scalable data science and analytics solutions that power business decision‑making and digital products. This role bridges data science development, distributed data processing, and cloud platform engineering—ensuring that models, rules, and analytics workflows are performant, secure, observable, and production‑ready on enterprise cloud platforms.

WHAT I DO? | Key Accountabilities.

Data Science & Distributed Engineering

• Design and develop Spark‑based (PySpark) modules implementing custom data science logic, statistical methods, and complex business rules.
• Build reusable, scalable Python and SQL components for batch and near‑real‑time analytics workloads.
• Optimize data pipelines for performance, reliability, and cost efficiency across large datasets.
• Translate analytical and data science requirements into production‑grade, testable code.

Data Science Platform & Cloud Enablement

• Contribute to and support the Applied Data Science hosting Spark workloads and web applications on GCP and/or Azure.
• Configure and manage cloud resources including:
• Virtual Private Clouds (VPCs) and networking
• Secure access patterns and Role‑Based Access Control (RBAC)
• IP whitelisting and external exposure of applications
• Enable observability through logging, monitoring, alerting, and traceability for both data and application workloads.

Infrastructure, CI/CD & Automation

• Implement and maintain CI/CD pipelines for data science and Spark workloads.
• Use Docker for containerized execution and deployment.
• Apply Terraform / IaC principles to provision and manage cloud infrastructure.
• Ensure consistent, repeatable deployments across development, staging, and production environments.

Engineering Quality & Governance

• Enforce best practices around code quality, testing, version control, and documentation.
• Partner with security and platform teams to ensure compliance with enterprise standards.
• Troubleshoot production issues across data pipelines, Spark jobs, and platform components.

Collaboration & Technical Leadership

• Work closely with Data Scientists, ML Engineers, Platform Engineers, and Product teams to operationalize analytics solutions.
• Provide technical guidance and reviews for complex data science engineering work.
• Contribute to platform standards, reusable frameworks, and engineering roadmaps.

HOW I DO MY JOB? | Qualifications.

Experience

• 5–8+ years of hands‑on experience in data engineering, data science engineering, or analytics platform engineering.
• Proven experience delivering production systems built on Python, PySpark, BigQuery and SQL.

Core Technical Skills

• Strong proficiency in:
• Python (production‑level code)
• PySpark / Apache Spark
• SQL across large analytical datasets
• Experience implementing complex analytical logic, business rules, or statistical workflows at scale.

Cloud & Platform Expertise

• Hands-on experience with GCP and/or Azure, including:
• Cloud consoles and managed services
• Networking concepts (VPCs, subnets, firewall rules)
• RBAC, IAM, and secure access controls
• Experience exposing internal applications securely to external consumers.

DevOps & Infrastructure

• Practical knowledge of:
• CI/CD pipelines
• Docker and containerized workloads
• Terraform / Infrastructure as Code
• Strong understanding of production reliability, monitoring, and observability.

Software & Systems Engineering

• Solid grounding in software engineering principles, modular design, and testing strategies.
• Ability to debug and operate distributed systems in production.

Leadership & Mindset

• Comfortable operating in a mixed role: hands-on development and platform enablement.
• Strong ownership mindset with the ability to work across data science, infra, and engineering boundaries.
• Clear communicator able to align technical solutions with business needs.