Build and optimize data pipelines with automated testing, lineage tracking, and privacy-by-design principles

Design, implement, and maintain data warehouses using SQL, Python, and ELT processes

Set up monitoring, testing, backup, and recovery mechanisms to ensure data reliability and availability

Enforce governance, security standards, role-based access control (RBAC), and data masking protocols

Translate business needs into scalable and reusable data solutions

Standardize ingestion and transformation processes for machine learning workflows, including feature preparation and monitoring drift and quality

Develop and deploy scalable machine learning models and pipelines

Create reusable components and maintain detailed documentation for data systems

Promote reliability, maintainability, and scalability in data workflows while optimizing pipelines for AI training and evaluation

Advocate for best practices and reusable patterns across teams

Contribute to onboarding processes and build communities of practice within the organization

Resolve complex challenges, perform root cause analyses, and enhance engineering standards

Make informed decisions, escalate issues when necessary, and improve scalability and cost efficiency of data systems

Align and influence cross-functional teams with minimal supervision

Mentor peers and support the development of team capabilities

At least 3 years of proven experience in Data Engineering

Expertise in SQL and ELT design, including core SQL, CDC patterns, and optimization techniques

Proficiency in Snowflake performance tuning, secure data sharing, role-based access, and data masking

Experience in developing Matillion jobs, shared components, and orchestration processes

Knowledge of CI/CD workflows using Bitbucket and Jenkins for building, testing, deploying, and managing environments and versions

Familiarity with data observability practices, including logging, alerts, and automated checks

Hands-on experience with feature engineering for machine learning, dataset validation, and management of data contracts and roles

Practical experience with EMR / Apache Spark for distributed data processing

Proficiency in using AWS for scalable data ingestion, processing, and preparation for machine learning pipelines

Ability to apply RAG principles for policy-aware grounding and design logical and physical data models

Skilled in maintaining documentation, APIs, and reusable patterns while supporting onboarding and best practices

Fluent English communication skills, written and spoken, at a B2+ level or higher

Knowledge of real-time data streaming technologies like Apache Kafka or AWS Kinesis

Exposure to Big Data ecosystems and tools such as Hadoop or Hive