Description
Invent the future with us
Ampere is a semiconductor design company for a new era, leading the future of computing with an innovative approach to CPU design focused on high-performance, energy efficient AI compute.
As a pioneer in the new frontier of energy efficient high-performance computing, Ampere is part of the Softbank Group of companies driving sustainable computing for AI, Cloud, and Edge applications.
Join us at Ampere and work alongside a passionate and growing team - we'd love to have you apply!
About the role
You will join the Manufacturing Test Engineering (MTE) Software team as a Data Engineer, responsible for developing and operating scalable data pipelines for manufacturing test data.
You will work with data from Automated Test Equipment (ATE), System Level Test (SLT), test automation platforms, logs, databases, files, and internal services. Your work will make manufacturing test data reliable, accessible, and ready for engineering analysis, reporting, operational insights, and downstream applications.
This is a hands-on technical role. You will use Databricks, Apache Spark, Python, PySpark, and SQL to build data solutions based on a Bronze-Silver-Gold (Medallion) architecture. You will collaborate closely with manufacturing test engineers,
software engineers, analysts, and other stakeholders to improve test-data quality, traceability, and usability.
The role is aligned with MTE Software's mission to build high-quality engineering tools and platforms in close collaboration with manufacturing and engineering teams.
What you'll achieve
Build Manufacturing Test Data Pipelines
- Design, develop, test, deploy, and maintain scalable data pipelines for manufacturing test data.
- Ingest data from ATE, SLT, test automation systems, logs, databases, files, and internal services.
- Develop robust ETL/ELT workflows using Databricks, Python, PySpark, and SQL.
- Build primarily batch data pipelines and, where required, near-real-time pipelines to support manufacturing and engineering use cases.
- Work with test engineers and software teams to understand test flows, data formats, schemas, source-system behavior, and downstream requirements.
- Ensure pipelines can process high volumes of structured and semi-structured test data reliably and efficiently.
Develop Bronze-Silver-Gold Data Architecture
- Build and maintain the Bronze layer for raw data ingestion, retention, source traceability, metadata capture, and auditability.
- Build the Silver layer to clean, validate, standardize, deduplicate, enrich, and model manufacturing test data into trusted datasets.
- Build the Gold layer to deliver curated, analytics-ready datasets, aggregations, and data models for engineering analysis, dashboards, reporting, and applications.
- Define reusable source-to-target mappings, data models, schema conventions, naming standards, documentation, and data ownership practices.
- Ensure test data is accurate, consistent, available, and suitable for both batch and real-time analytics workloads.
Ensure Quality, Performance, and Reliability
- Implement data-quality checks, reconciliation processes, schema validation, logging, monitoring, alerting, retries, and error-recovery mechanisms.
- Investigate and resolve data-ingestion failures, transformation issues, data-quality defects, latency issues, and downstream data-consumption problems.
- Optimize Databricks workloads, Spark jobs, storage design, partitioning, and pipeline architecture for scalability, performance, reliability, and cost efficiency.
- Write maintainable, well-tested, production-quality code and actively participate in code reviews.
- Contribute to unit testing, integration testing, CI/CD pipelines, automated deployment, release processes, and operational support practices.
- Apply secure engineering practices for authentication, authorization, secrets management, data access control, governance, and compliance.
Collaborate with Cross-Functional Teams
- Work directly with manufacturing test engineers, software engineers, data analysts, and stakeholders to clarify requirements and delivery scope.
- Translate ambiguous requirements into data models, technical designs, implementation tasks, acceptance criteria, and phased delivery plans.
- Partner with teams developing engineering applications, APIs, dashboards, and analytics tools to provide reliable and efficient datasets.
- Participate in design discussions, code reviews, and technical troubleshooting to maintain high engineering quality.
- Contribute reusable pipeline patterns, data-quality checks, documentation, and coding standards to support the MTE Software ecosystem.
About you
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 5+ years of professional experience in data engineering, software engineering, analytics engineering, or a related technical field.
- Strong hands-on production experience with Databricks and Apache Spark.
- Proven experience implementing a Bronze-Silver-Gold / Medallion data architecture.
- Strong proficiency in Python, PySpark, and SQL.
- Experience designing, developing, and supporting production ETL/ELT pipelines.
- Strong understanding of data modeling, data warehousing, ETL/ELT processes, and data-pipeline architecture.
- Experience processing large volumes of structured and semi-structured data.
- Experience with at least one cloud platform, such as AWS, Azure, or GCP.
- Experience implementing data validation, pipeline monitoring, logging, alerting, troubleshooting, and production incident resolution.
- Familiarity with Git-based development, CI/CD, automated testing, and deployment practices.
- Strong problem-solving, communication, and cross-functional collaboration skills.
- Ability to work independently, own assigned technical deliverables, and collaborate effectively with cross-functional teams.
- Preferred qualifications:
- Experience with Delta Lake, Delta Live Tables (DLT), Databricks Workflows, Unity Catalog, and Databricks SQL.
- Experience with streaming technologies such as Spark Structured Streaming, Kafka, Azure Event Hubs, or AWS Kinesis.
- Experience with workflow orchestration tools such as Databricks Workflows, Apache Airflow, Azure Data Factory, or similar.
- Experience with infrastructure-as-code tools, such as Terraform.
- Experience working with semiconductor, manufacturing, ATE, SLT, hardware validation, telemetry, or other high-volume operational data.
- Experience supporting BI, reporting, analytics, data-intensive engineering applications, or machine-learning workloads.
What we'll offer
At Ampere we believe in taking care of our employees and providing a competitive total rewards package that includes base pay, cash long-term incentive, and comprehensive benefits.
Benefits highlights include:
- Premium healthcare, personal accident, and fully paid social insurance scheme as well as annual health check, so that you can feel secure in your health and financial future.
- Generous paid time off policy so that you can embrace a healthy work-life balance.
- Daily catered lunch, a variety of snacks, energizing coffee or tea, and refreshing drinks to keep you fueled and focused throughout the day.
And there is much more than compensation and benefits. At Ampere, we foster an inclusive culture that empowers our employees to do more and grow more. We are excited to share more about our career opportunities with you through the interview process.
Ampere is an inclusive and equal opportunity employer and welcomes applicants from all backgrounds. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, religion, age, veteran and/or military status, sex, sexual orientation, gender, gender identity, gender expression, physical or mental disability, or any other basis protected by federal, state or local law.