Senior Data Engineer

Intercontinental Exchange Holdings, Inc.•Atlanta, GA

8d•Onsite

About The Position

ICE Data Services (an Intercontinental Exchange company) is seeking a Senior Data Engineer to join its Data Impact & Innovation team. This team supports a variety of reference data, index, climate finance, and alternative data products. The role contributes to the data platforms and pipelines that help the financial sector understand and respond to carbon transition risk, physical risk, and related challenges. Our team maintains a global-scale geospatial data platform in Google BigQuery, holding many terabytes of data across carbon transition risk, physical climate risk, and social/demographic features — feeding analytical products for fixed income and real estate financial instruments, supporting the ambitious product roadmap for ICE Climate and other data products. Our engineering stack includes: Orchestration: Airflow, moving toward composable task abstractions over a shared pipeline framework Transformation: dbt, and other data lineage and DQA tools, primarily using Google BigQuery Geospatial processing: Python (GeoPandas, Shapely, GeoAlchemy2 against PostGIS) for vector operations, and R Execution and compute environments: Hybrid across Google Cloud Platform and on-premise RHEL Linux infrastructure Ingestion: Third-party vendor feeds via API, SFTP, cloud storage, and database replication Typical engineering challenges include working with data science and climate science teams in operationalizing trained models and data pipelines, absorbing upstream vendor corrections and historical restatements without corrupting downstream artifacts, scaling raster × vector joins at terabyte scale, evolving schemas and spatial-indexing strategies as data sources broaden, and balancing long-running batch workflows against emerging sub-daily refresh cadences.

Requirements

5+ years of professional experience as a data engineer, with a track record of architecting, shipping, and operating production data pipelines end-to-end.
Experience mentoring and developing other data engineers — through code review, pairing, design discussions, and career coaching.
Ability to establish and foster adoption of technical standards.
A habit of actively monitoring, evaluating, and prototyping emerging big-data, geospatial, and machine-learning technologies and platforms — staying conversant in advances across cloud data engines, geospatial libraries and standards, and ML/MLOps frameworks — and bringing the most promising into the team’s design discussions, evaluations, and adoption decisions.
Strong system-design judgment across the tradeoff space of performance, cost, maintainability, and auditability
Comfort scoping, decomposing, and delegating work for other engineers.
Strong written and verbal communication — able to translate technical tradeoffs for senior business, product, and client stakeholders.
Deep fluency in modern, typed Python as a primary working language, including comfort with type-driven design (e.g. Pydantic v2).
Strong SQL background, including experience partitioning, clustering, and performance-tuning queries on modern cloud warehouses — Google BigQuery experience strongly preferred.
Production experience with dbt for managing warehouse transformations, and with Airflow (or a comparable orchestrator) for workflow orchestration.
Solid grounding in geospatial data engineering — Python tooling (GeoPandas, Shapely), spatial databases (PostGIS), raster processing, or adjacent skills.
A systems-thinking orientation: anticipates cascading effects of upstream data changes, schema evolution, and vendor corrections; designs pipelines with observability, auditability, and graceful failure in mind.
Comfort owning production incidents and debugging distributed systems.
Experience working cooperatively with systems, network, and infrastructure engineering and operations teams to ensure proper monitoring, alerting, and incident response workflows.
Demonstrated ability to integrate AI/LLM coding assistants productively — treating them as a force multiplier rather than a substitute for judgment.
Curiosity about the financial and climate/geospatial domains and contexts the team operates in.

Nice To Haves

Well-versed in and opinionated about the modern Python ecosystem.
Exposure to columnar and lakehouse technologies (Parquet, ClickHouse, DuckDB).
Working understanding of data lineage, data quality validation, and metadata/cataloging frameworks.
Prior experience in a hybrid cloud + on-premise environment, and with full software development lifecycle (SDLC) best practices and processes.
Prior exposure to ML deployment workflows — supporting data science teams with training tooling and/or model-serving infrastructure.
Familiarity with R, particularly geospatial packages.

Responsibilities

Take significant components of the data platform from “works” to “mature” — tightening reliability, observability, cost/performance characteristics, and operational discipline across our ingestion, transformation, and serving layers.
Establish and foster adoption of technical standards for the team’s work — including Airflow DAG structure, dbt model layout, BigQuery schema and partitioning conventions, pipeline testing practices, and deployment workflows.
Lead technical design discussions, mentor other data engineers through code review, pairing, and design-doc review, and grow them along their career path.
Act as a technical point of contact for cross-functional initiatives — partnering with data science, climate science, product, and infrastructure colleagues to drive forward decisions and make tradeoffs explicit.
Deliver day-to-day work across the stack above — authoring Airflow DAGs and dbt models, contributing geospatial processing capabilities, and shipping cleanly partitioned, audit-friendly outputs from ingestion through serving.
Support data science and climate science teams by helping design the tooling, training, and validation environments, and by deploying their trained models into production.
Effectively leverage AI and LLM-based developer tooling to accelerate development workflows and improve code quality.
Identify opportunities to improve and optimize data pipelines — for speed, cost, robustness, integrity, and operational simplicity.
Work with business analysts, product management, and adjacent engineering teams to understand and refine new data requirements.