Data Engineer
IBM
Denver, CO1d agovia LinkedIn
This job at a glance
- Who can apply
- New grads, no experience needed
- Must know
- Embedded systemsMySQLETLCI/CDStakeholder managementPython
- Nice to have
- AWSSparkSAPGCP
- Work
- On-site in Denver
- Pay
- Not stated by the employer.
- Skill demand
- ETL74%CI/CD38%MySQL3%Embedded systems1%of 100 similar entry-level jobs posted in the US this week
- Listed
- Oct 27, 2026 · still open
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About the Role
A Data Engineer will be embedded with client teams to design and deliver hands-on solutions on the AWS/Databricks platform. This role combines architecture and engineering, involving building and proving solutions with the client. Work includes re-engineering legacy data workloads and supporting data migration from MySQL to Databricks Lakebase.
Key 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.
Requirements & Tech Stack
- 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, optimization, 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.