Data Engineer - EDP

Data Engineer, Enterprise Data Platform (EDP)

Position Summary

The Data Engineer, Enterprise Data Platform (EDP) is a hands-on engineer responsible for building and operating the ingestion and curation layers of Bridgestone's multi-cloud Enterprise Data Platform. The EDP is a platform-as-a-service: business units build their own data products on top of it, within guardrails and governance standards this role applies and helps uphold. The platform runs on a Databricks and Unity Catalog backbone spanning AWS and Azure, delivering curated data to business units, BI and analytics teams, and conversational query experiences.

This role writes code every day. It builds pipelines to the technical standards set by the platform team, and works alongside Platform Engineering, Solution Engineers, Product Owners, and business unit engineering teams. Hands-on Databricks experience and strong data engineering fundamentals are essential.

Job Duties

Builds and operates industrialized data ingestion and transformation pipelines from source systems through the silver layer of a medallion architecture, using Databricks (Spark, Unity Catalog, Workflows) as the core platform. Develops in Databricks as the primary environment — data modeling in Delta, job and cluster configuration, and performance and cost tuning within established platform policies. Builds and maintains pipelines across multiple clouds — primarily AWS (Glue, Step Functions, Redshift, Aurora, Transfer Family, S3) and Azure (Data Factory, ADLS) — and executes the migration of native cloud pipelines to Databricks as the destination technology. Applies the platform’s guardrails, standards, and reusable patterns in day-to-day delivery, and raises the cases where an existing standard does not fit the problem so the pattern can be improved. Participates in design and code reviews, incorporates feedback, and contributes reusable components back to the shared platform codebase. Delivers curated, well-modeled, and well-documented data to the BI and analytics teams and to conversational query experiences such as Databricks Genie Space. Builds and maintains the curated tables, column and table documentation, and metric definitions that Genie Spaces depend on. When a Genie answer is wrong, investigates and corrects the underlying data model rather than patching the question. Uses AI tooling in pipeline development, testing, and documentation within the review standards set by the team, and verifies output before it reaches production. Implements data quality checks, lineage capture, and access controls in line with governance and security standards. Writes and maintains automated tests, CI/CD pipelines, and infrastructure as code for the pipelines and components this role owns. Monitors production pipelines, responds to data incidents, performs root cause analysis, and documents RCAs and SOPs to stabilize daily operations. Documents pipelines, data contracts, and operational runbooks so that work is supportable by others on the team. Other duties as assigned.

Required Qualifications

Bachelor's degree in computer science, computer engineering, information systems, or equivalent work experience. Minimum of 3 years in data engineering or IT development, including 2 years of cloud data engineering in a production environment. Hands-on Databricks experience — Spark (PySpark and SQL), Delta Lake, and Unity Catalog. Must have built and operated production pipelines in Databricks, not only used notebooks for analysis. This is the core technology of the platform. Strong data engineering fundamentals: data modeling, medallion architecture patterns, partitioning, incremental and CDC ingestion, idempotency, schema evolution, and backfill strategy. Demonstrated hands-on pipeline development — not solely support, monitoring, or coordination. This role builds. Production experience in at least one major cloud (AWS or Azure), including managed data services, storage, and the basics of identity and access management, with the ability to work across both. Strong Python and SQL. Experience with pipeline orchestration (Databricks Workflows, Step Functions, Azure Data Factory, or equivalent), including dependency management and scheduling. Working knowledge of data governance concepts — cataloging, lineage, data quality, and access control. Experience with Git and CI/CD (Azure DevOps or equivalent) to promote code and release packages through environments. Ability to perform end-to-end testing and debug issues across distributed cloud services. Clear written and verbal communication — able to document work, explain a technical constraint to a non-specialist, and raise blockers early rather than late. Practical experience with Agile delivery practices. Uses AI tooling as part of a normal working day rather than as an occasional experiment. Candidates will be asked to walk through their own workflow in the interview: which tools, at what points in the work, what they verify before trusting the output, and what actually got faster as a result.

Preferred Qualifications

Databricks certification (Data Engineer Associate or Professional) or major cloud certification. Hands-on exposure to Databricks Genie — curating the underlying tables, writing space instructions and sample queries, and defining metrics. Experience with Databricks Asset Bundles and Terraform. Production experience across both AWS and Azure. Working knowledge of ingestion from ERP and legacy sources (SAP CPI/SLT/PO, SAP BW, Teradata) into cloud or on-premises databases. Exposure to AWS Redshift, Aurora, Glue, Athena, Kinesis, SNS, SQS, DynamoDB, and Azure Synapse. Experience working in a large enterprise environment: matrixed teams, multiple business units with differing data maturity, change management, and formal release processes.

Working Hours

Aligned to Bridgestone Nashville Head Office business hours, with overlap at least until 12PM Central Time Zone with rotating shifts to cover US Central time till 4PM CST.