ICE) is seeking an experienced Senior Data Engineer to join the AI Centre of Excellence (AI CoE) team. The AI CoE drives the adoption of artificial intelligence and advanced analytics across ICE's global exchange and financial data business, bringing together data engineering, machine learning, and applied research to deliver intelligent solutions at scale. This role sits at the foundation of that work, ensuring the data infrastructure, pipelines, and platforms the AI CoE depends on are robust, scalable, and fit for purpose.

The successful candidate will take ownership of the orchestration layer that keeps our data moving reliably, contribute to the lakehouse architecture that provides clean and trusted data to analytical and ML consumers, and develop the transformation logic that converts raw inputs into high-quality, governed data products. The role requires close collaboration with data scientists, ML engineers, and business analysts, and carries meaningful input into the tooling decisions and engineering standards the team adopts.

We are looking for a candidate with demonstrable, production-scale experience in data engineering and pipeline orchestration. You will write DAGs that handle complex multi-step workflows across ingestion, transformation, validation, and delivery. That means getting the fundamentals right: idempotency, backfill safety, SLA alerting, dynamic task mapping, and thorough testing. Keep Airflow running well on Kubernetes. You will manage the deployment using KubernetesExecutor or CeleryKubernetesExecutor, handle Helm upgrades, tune pod templates and resource limits, and deal with scheduler performance issues before they become incidents.

Build ETL and ELT pipelines that hold up in production. APIs, message queues, databases, object storage.



Treat data quality as part of the job, not an afterthought. Schema validation, row-count reconciliation, and anomaly detection should be baked into pipelines from the start. Design and maintain a lakehouse architecture using Apache Iceberg as the primary table format, following a Bronze, Silver, and Gold medallion structure. Schema evolution, partitioning strategies, and time-travel queries will be regular concerns, not edge cases.

Build and maintain dbt projects that sit alongside the Airflow orchestration layer. Partner with data scientists and ML engineers to deliver feature pipelines and training datasets with the freshness and reproducibility their models need.

You will own runbooks and participate in on-call cover.

Your experience should benefit the team, not just your own workstream.

Knowledge And Experience

Airflow on Kubernetes: Real experience deploying and operating Airflow on Kubernetes via KubernetesExecutor or CeleryKubernetesExecutor: pod templates, resource tuning, persistent volume claims, Helm chart management, and upgrades that do not take the scheduler offline.

Strong Python applied to DAG authoring: dynamic task mapping, custom operators and sensors, cross-DAG triggers, parameterised pipelines, and proper test coverage. Writing testable DAGs should feel natural, not optional.

ETL and ELT Pipelines:



Demonstrated experience building batch and incremental pipelines at scale. You understand transformation logic, data quality validation, and lineage capture, and you have shipped pipelines that other people maintain without you.

Data Lakehouse Architecture: A solid grasp of lakehouse design in practice: how to implement a medallion model, when to use which table format, and how to make data reliably available to downstream consumers without constant hand-holding.

Hands-on knowledge of the Iceberg table spec: snapshot isolation, partition evolution, hidden partitioning, and time-travel queries. Practical Databricks experience covering cluster management, Delta Lake, Workflows or Jobs, and Unity Catalog. Experience with Databricks Asset Bundles or MLflow is a welcome bonus.

Confident building dbt projects from the ground up: models, sources, tests, snapshots, seeds, and macros. Python: Robust engineering-grade Python: well-structured, tested, and packaged. Preferred Knowledge And Experience

Experience with near-real-time ingestion using Kafka, Kafka Connect, or Flink, including landing data into lakehouse storage.

Kubernetes and OpenShift: Data Governance and Contracts: Familiarity with data contract approaches, catalogue tools such as DataHub or Collibra, and quality frameworks like Great Expectations or Soda.

Cloud Platforms: Experience with managed data services on AWS, Azure, or GCP: S3, Glue, ADLS, Synapse, GCS, Dataproc, and similar.

Infrastructure as Code: Ability to provision data infrastructure using Terraform or Pulumi.

ML Feature Engineering: An understanding of feature stores and experience building pipelines that serve model training and inference reliably.

📌 Senior Data Engineer - AI & Data (Secunderabad)
🏢 ICE
📍 Secunderabad