Job Description:
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
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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).
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Database Engineering: Model operational tables; write optimized SQL queries (joins, window functions, CTEs); implement indexing and partitioning; and manage data migrations.
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Data Validation & Quality Control: Implement logical and business-rule validations for Sustainable Finance datasets to ensure accuracy and completeness.
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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.
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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.
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Documentation & Traceability: Maintain comprehensive documentation—data lineage, validation rules, SLAs, and operational playbooks—to support audits and client transparency.
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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
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Bachelor’s or Master’s degree in computer science, Information Systems, Data Science, Business Analytics, or a related field.
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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.
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Proven experience integrating Python applications with backend databases (PostgreSQL/MySQL/SQL Server), implementing CRUD operations, and managing batch/stream ingestion.
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Solid understanding of data quality techniques and auditability.
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Cloud & Workflow Orchestration
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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.
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Familiarity with AWS or GCP cloud platforms to build and manage data pipelines, with exposure to big data frameworks and data transformation tools.
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Data Visualization & Web Data Acquisition
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Experience with data visualization tools (Power BI/Tableau or equivalent).
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Experience building web scrapping for company reports and implementing publication-date policies.
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Software Engineering Practices
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Knowledge of version control (Git), testing practices (unit tests), and CI/CD concepts.
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Generative AI & Automation
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Experience integrating Gen AI agents into existing ETL/ELT pipelines or operational data workflows.
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Proficiency with AI workflow automation platforms such as N8N.
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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.
