Job Purpose
Intercontinental Exchange (ICE) is seeking a highly motivated and detail-oriented Data Engineer to enhance Sustainable Finance Operations through automation and robust data quality practices. In this role, you will design and develop Python-based applications and data pipelines that ingest ESG documents and market data, store them in backend databases, perform rule-based and statistical validations, and deliver clean, reliable datasets for downstream products and operational workflows.
Responsibilities
- Automation & Application Development: Design, develop, and maintain Python-based applications and ETL/ELT pipelines (e.g., ingest data from APIs and company websites, parse sustainability/ESG reports, and persist data in SQL backends).
- Database Engineering: Model operational tables; write optimized SQL queries (joins, window functions, CTEs); implement indexing and partitioning; and manage data migrations.
- Data Validation & Quality Control: Implement logical and business-rule validations for Sustainable Finance datasets to ensure accuracy and completeness.
- Workflow Orchestration: Schedule and monitor jobs using AWS/GCP orchestration tools (e.g., AWS Glue, Step Functions, GCP Dataflow) or similar; implement alerting and recovery runbooks.
- Data Visualization Support: Collaborate with Operations to publish curated datasets and build dashboards (Power BI/Tableau or equivalent) for tracking coverage, timeliness, and quality KPIs.
- Documentation & Traceability: Maintain comprehensive documentation—data lineage, validation rules, SLAs, and operational playbooks—to support audits and client transparency.
- Cross-Functional Collaboration: Work closely with Operations leads and product partners to resolve data issues, implement corrections, and continuously improve throughput and accuracy.
Knowledge and Experience
- Bachelor’s or Master’s degree in computer science, Information Systems, Data Science, Business Analytics, or a related field.
- 2+ years of experience in Data Engineering or related roles with strong proficiency in Python (Pandas, Numpy, SQL Alchemy/pyodbc, OOPS) and production-grade SQL.
- Proven experience integrating Python applications with backend databases (PostgreSQL/MySQL/SQL Server), implementing CRUD operations, and managing batch/stream ingestion.
- Solid understanding of data quality techniques and auditability.
- Cloud & Workflow Orchestration
- Familiar knowledge with workflow orchestration tools (Apache Airflow, AWS Step Functions, or GCP Cloud Composer) to schedule, monitor, and recover data pipelines with alerting mechanisms in place.
- Familiarity with AWS or GCP cloud platforms to build and manage data pipelines, with exposure to big data frameworks and data transformation tools.
- Data Visualization & Web Data Acquisition
- Experience with data visualization tools (Power BI/Tableau or equivalent).
- Experience building web scrapping for company reports and implementing publication-date policies.
- Software Engineering Practices
- Knowledge of version control (Git), testing practices (unit tests), and CI/CD concepts.
- Generative AI & Automation
- Experience integrating Gen AI agents into existing ETL/ELT pipelines or operational data workflows.
- Proficiency with AI workflow automation platforms such as N8N.
- Hands-on experience with Generative AI concepts and familiarity with LLMs (e.g., Anthropic Claude, or open-source models) and their practical applications in data workflows.
