Data Engineer

About Rimes

Rimes delivers an Intelligence Fabric designed for Capital Markets, serving as a reliable data network and intelligence structure that converts diverse datasets, operations, and workflows into actionable, decision-quality intelligence. It supports leading institutional investors, asset managers, and service providers globally, facilitating investment decisions for assets exceeding US$75 trillion annually.

Role Overview

We are seeking a Data Engineer to help reconstruct and modernize the data platform that supports our extensive data ecosystem. Reporting to the Data Engineering Team Lead, you will engage hands-on in platform development, tooling, data modeling, and operational enhancements.

This role focuses on creating reusable and scalable capabilities enabling efficient construction of high-quality financial data pipelines, rather than building pipelines individually. Interest in agentic AI workflows that can automate and improve data platform operations is also highly valued.

Key Responsibilities

• Contribute to the modernization of existing data platforms and pipelines using advanced technologies like Snowflake and Databricks to enhance scalability, efficiency, and performance.
• Develop and improve tools to facilitate seamless financial data ingestion and quality assurance, automating repetitive tasks to optimize operational workflows.
• Design and implement scalable, reusable data models aligning with diverse business use cases, maintaining consistency and long-term value of data products.
• Engage in daily engineering tasks including coding, reviewing, and architecting complex data solutions while promoting knowledge sharing and engineering excellence.
• Implement methods to minimize operational costs related to data ingestion and pipeline maintenance by optimizing technical workflows and support processes.
• Collaborate closely with Product, Data Onboarding, Data Quality, and Operations teams to ensure data engineering solutions satisfy both technical standards and business objectives.
• Apply expertise in financial data—such as pricing, benchmarks, reference data, and corporate actions—to optimize data models and tooling compliant with domain requirements.
• Investigate and prototype agentic workflow models where autonomous agents manage data pipelines based on data triggers and events, integrating cutting-edge large language model (LLM) tools to enhance platform automation.

Requirements

Professional Experience:

• 3 to 5 years of practical experience in data engineering or a closely linked field.
• Proven ability to develop shared tools, frameworks, or reusable components beyond individual pipeline development.
• Experience in financial or enterprise data environments is advantageous.

Technical Skills:

• Excellent proficiency in Python with capability to produce production-ready, thoroughly tested code.
• Advanced skills in SQL for data modeling, query optimization, and analytical tasks.
• Hands-on experience with Databricks and Apache Spark, including Delta Lake, Spark SQL, and cluster optimization for large-scale distributed processing.
• Familiarity with cloud data platforms such as Snowflake or similar warehouses (BigQuery, Redshift, Synapse) alongside Databricks.
• Experience with workflow orchestration tools like Airflow, Prefect, or Dagster.
• Knowledge of cloud infrastructure services from AWS, Azure, or GCP, including object storage, compute, serverless, and identity and access management (IAM).
• Competency with DevOps tools including Git, Docker, and CI/CD pipelines tailored for data platform deployments.
• Experience implementing data quality assurance through checks, schema validation, or contract testing.
• Proficiency in AI-assisted coding tools such as GitHub Copilot and Claude to streamline development, assist in code generation and review, and navigate complex codebases within daily workflows.

Preferred Qualifications:

• Practical experience with agentic AI frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI, Anthropic Agent SDK).
• Familiarity with streaming data systems such as Kafka, Kinesis, or Flink.
• Exposure to financial data types like pricing, reference data, benchmarks, indices, and corporate actions.
• Experience working with metadata catalogues such as Unity Catalog, DataHub, OpenMetadata, or Alation.
• Understanding of data contract frameworks and patterns.

Benefits and Work Environment

• Private Health Insurance coverage including eligible dependents.
• A generous annual leave entitlement of 23 days.
• Access to an Employee Assistance Programme for well-being support.
• Competitive salary package with eligibility for bonuses.
• Flexible hybrid working arrangement promoting work-life balance.

Additional Information

Only shortlisted candidates will be invited for interviews. Rimes is dedicated to fostering diversity and inclusion across all facets of employment, including recruitment, retention, career development, and training, valuing individuals regardless of their background or circumstances.