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# Data Engineer

- **Company:** IBM
- **Location:** New York, NY, United States
- **Contract:** Permanent contract
- **Skills:** Unity 3d, Amazon Web Services, Apache HTTP Server, Big Data, Continuous Integration, Information Engineering, Data Migration, Data Sharing, Data Systems, Relational Databases, Python (Programming Language), PostgreSQL, Machine Learning, MySQL, Online Transaction Processing, SAP (Applications), SAP IS-U, SAP NetWeaver Data Management, SQL Databases, Data Streaming, Unstructured Data, Cloud Platform System, Apache Spark, SAP Business Technology Platform, Git, Database Migration, Build Management, Data Lakes, Git Flow, Data Analytics, Apache Kafka, Data Management, Machine Learning Operations, Amazon Simple Queue Service (SQS), Terraform, Software Version Control, Data Pipelines, Databricks
- **Published:** October 28, 2026
- **Apply:** https://dejobs.org/x/x/0E71CC3965B746FCB1E9640569D99680/job/

## About the Role

Required technical and professional expertise

 * Must be able to travel 4 days per week (mandatory).

 * Databricks Certified Data Engineer or Machine Learning Professional.

 * Advanced hands-on expertise in data engineering and solution architecture, with demonstrated ability to design and deliver complex data solutions.

 * Strong hands-on expertise with Databricks (Spark, Delta Lake, MLflow, Unity Catalog), AWS, SQL, and Python, including large-scale batch and streaming workloads.

 * Experience with relational databases such as MySQL, database migration/re-engineering, Fivetran or similar ingestion/ELT technologies, and modern data-sharing patterns.

 * Experience with Delta Lake, Apache Iceberg, and other open table formats, including schema evolution, optimisation, and interoperability across engines.

 * Hands-on experience designing scalable data platforms and pipelines using technologies such as Kafka, Amazon EventBridge, or Amazon SQS, along with Terraform and CI/CD practices.

Preferred technical and professional experience

 * Experience with Databricks Lakebase or similar managed PostgreSQL/OLTP-on-lakehouse technologies.

 * Knowledge of SAP BTP, BDC Connect, SAP extraction patterns, or SAP IS-U.

 * Exposure to GCP ML/GenAI services and cloud-native solution design across AWS and GCP.

 * Experience with Git-based version control and branching strategies for data workloads.

 * Experience in the utilities or energy industry, with relevant Databricks and/or AWS certification.

## Description

Data Engineer embedded with the client's teams to design and deliver hands-on solutions on the AWS/Databricks platform. The role combines architecture and engineering, and you will build and prove solutions with the client. Work includes re-engineering legacy data workloads and supporting data migration from MySQL to Databricks Lakebase.

Responsibilities

 * Work onsite or embedded with client teams to understand business problems and turn them into working technical solutions.

 * Design and build data pipelines and integrations using Fivetran, Databricks, and AWS services.

 * Lead technical design and delivery for migrating a Tier-1 application from MySQL to Databricks Lakebase, including schema re-design, data migration, cutover, and validation.

 * Integrate SAP data through SAP BTP and BDC Connect, non-SAP data, and design domain centric data products.

 * Build prototypes and reference implementations, then work with the client to harden them for production.

 * Support data and AI use cases, including integration with GCP-based ML/GenAI components.

 * Establish engineering standards: CI/CD, testing, observability, security, and cost control.

 * Coach client and delivery team members, and communicate status, risks, and design decisions to stakeholders.

 * Design end-to-end data solutions across multiple platforms, applying industry standards and best practices to meet client needs.

 * Lead the development of data platform architectures, including cognitive components and unstructured data technologies, annotation, and related data and analytics.

 * Manage and maintain large data repositories, ensuring scalability and performance.

 * Create data solutions on cloud-native platforms (AWS, with GCP for ML/GenAI), tailored to client requirements.

 * Collaborate with clients to understand their needs and deliver customized data solutions that meet their business objectives.