Senior Data Engineer

Rithum is the world's most trusted commerce network, accelerating how brands, suppliers, and retailers work together to deliver seamless e-commerce experiences. We provide an unmatched platform for brands and retailers, enabling them to accelerate growth, optimise operations across channels, scale product offerings and enhance margins.

Today, more than 40,000 companies trust Rithum to grow their business across hundreds of channels, representing over $50 billion in annual GMV. Using our commerce, marketing, and delivery solutions, our customers create optimised consumer shopping journeys from beginning to end.

**Overview**

As a **Senior Data Engineer**, you are a key architect in developing, delivering and maintaining intelligent data infrastructure that powers advanced analytics. You are an experienced data engineer with strong ETL pipeline and dimensional modeling skills, who is ready to leverage AI as a powerful collaborator and brings a depth of knowledge to ensure it is employed effectively for the long-term health of our business systems. You are responsible for building complex data pipelines from the ground up, delivering data into and enhancing a database infrastructure that powers advanced analytics, AI-driven decision-making, and autonomous data operations. You work across the organization, supporting and collaborating with our product teams, engineers, data scientists, and data analysts. In this context, you enhance and support our company's cloud data architecture to support our products and data initiatives, for external clients as well as internal users.

**Responsibilities**
- Core Data Infrastructure_
- **Design and implement scalable ETL/ELT workflows** that support both batch and streaming data using AWS primitives (e.g., S3, Kinesis, Glue, Redshift, Athena)
- **Architect and maintain cloud-native data platforms** with automated ingestion, transformation, and governance pipelines using modern tools like DBT, Apache Spark, Delta Lake, Airflow, and Databricks
- **Work with stakeholders, including the Product, BI, and Support teams**, to assist with data-related technical challenges and support their data and infrastructure needs.
- **Collaborate with cross-functional Engineering teams** using analytics to predict data needs and proactively deliver solutions.
- **Assist in the optimization of data lake/lakehouse infrastructure**, to support AI workloads and large-scale analytics.
- Governance & Collaboration_
- **Ensure data quality, lineage, and observability**.
- **Develop and enforce data governance policies**, including compliance monitoring and privacy protection.
- **Partner with Data Scientists** to optimize data pipelines for model training, inference, and continuous learning workflows.
- Advanced Data Operations_
- **Build self-healing data pipelines** with AI-driven error detection, root cause analysis, and automated remediation capabilities.
- **Implement intelligent data lineage tracking** to automatically discover relationships between datasets and predict downstream impact of changes.
- **Create AI-assisted data discovery systems** that help stakeholders find relevant datasets and understand data semantics through natural language interfaces.
- **Participate in on-call rotation**, as needed.
- AI-Enhanced Development_
- **Leverage AI coding assistants** (GitHub Copilot, Cursor, etc.) to
- **Accelerate development** cycles, generate complex SQL queries, and automatically optimize data pipeline code.
- **Develop data quality monitoring**, using anomaly detection and data profiling tools to identify issues, before they impact downstream systems.
- **Optimize pipeline orchestration**, with ML to predict optimal scheduling, resource allocation, and failure recovery patterns.
- **Generate and maintain living documentation** that evolves with code changes.
- Leadership & Innovation_
- **Participate in the full software development lifecycle**, including both manual and AI-assisted requirements gathering, automated testing, and intelligent deployment strategies.
- **Mentor junior engineers** on both traditional data engineering practices and effective use of AI development tools.
- **Lead tool evaluation and adoption** for the data engineering team, establishing best practices for human-AI collaboration.
- **Drive innovation in data architecture** by experimenting with emerging technologies.

**Qualifications**

Minimum Qualifications
- 3+ years of experience in data engineering, including building and maintaining large-scale data pipelines
- Extensive experience in SQL RDBMS (SQLServer or similar) with dimensional modeling using star schema, and foundational data warehousing concepts
- Hands-on experience with AWS services such as Redshift, Athena, S3, Kinesis, Lambda, Glue
- Experience with DBT, Databricks or similar data platform tooling
- Experience working with structured and unstructured data and implementing data quality frameworks
- Excellent communication and collaboration