Note: The job is a remote job and is open to candidates in USA. Meso Scale Diagnostics, LLC. is a company focused on providing diagnostics solutions, and they are seeking a Senior Data Engineer to manage and organize data to support business processes. The role involves developing data pipelines, maintaining data structures, and collaborating with various teams to ensure high-quality data is available for analytics and decision-making.
Responsibilities
- Develop data pipelines from various data sources to target locations — including MES, ERP, PLM, SCADA/historian, and quality systems — to support the enterprise Digital Thread, including formatting, cleaning, and updating data as the business needs
- Develop and maintain accurate data structure and mapping documentation, including metadata aligned to specific business requirements
- Design and publish domain-oriented, reusable data products aligned to a data mesh model with clear ownership, SLAs, and discoverability — standardizing data structure and types across ETL/ELT processes
- Understand the big picture, set the right scope, and collaborate with the data architect, application engineers, integration specialists, business analysts, and other technical experts to ensure adequate content delivery to authorized users in a timely, effective, and secure manner
- Manage the full life cycle development for the current ETL/ELT deployments, applying software-engineering practices to data pipelines — version control (Git), automated testing, code review, CI/CD, and Infrastructure-as-Code (e.g., Terraform, CloudFormation) — to ensure repeatable, auditable deployments across environments
- Partner with manufacturing operations, quality, and supply chain teams to deliver analytics on OEE, yield, scrap, throughput, engineering-change cycle time, time-to-release, and end-to-end product genealogy/traceability supported by the Digital Thread
- Prepare AI/ML-ready datasets — including feature pipelines, dataset versioning, and curated knowledge sources for predictive quality, anomaly detection, and retrieval-augmented (RAG) use cases — in partnership with data science and AI teams
Skills
- Bachelor's degree in Computer Science, Software Engineering, or other related science or engineering discipline is required
- A minimum of four years experience in Data Modeling, Data Solution Development, and Data Integration
- Extensive experience working with data science tools/technologies, particularly Python, SQL, and/or C# .NET
- Experience analyzing business requirements, planning, executing actions, and solving complex problems
- Hands-on experience with the AWS data stack (S3, Glue, EMR, Redshift, Lake Formation, Kinesis, Lambda, and IAM) for building, securing, and operating production data platforms is required
- Advanced degree preferred
- Experience integrating data from manufacturing and operational systems (MES, ERP, PLM, SCADA/historian, LIMS, or QMS) is highly preferred
- AWS Certified Data Engineer or AWS Certified Data Analytics certification is highly preferred
- Experience contributing to a data mesh, data fabric, or Digital Thread initiative — including building domain-oriented data products, using a data catalog, and applying federated governance — is highly preferred
- Experience implementing data governance, lineage, and cataloging on AWS (e.g., AWS Glue Data Catalog, Lake Formation, and tools such as Collibra, Alation, Atlan, or OpenMetadata) is highly preferred
- Knowledge of ETL/ELT process tools, such as SSIS, Informatica, Talend, dbt, Fivetran, and/or Airflow is highly preferred
- Working knowledge of DataOps practices and tooling — including Git-based workflows, CI/CD (e.g., GitHub Actions, GitLab CI, AWS CodePipeline), Infrastructure-as-Code (Terraform or CloudFormation), automated data testing, and pipeline observability — is highly preferred
- Working knowledge of modern data platform technologies — such as cloud data warehouses/lakehouses (Snowflake, Databricks, BigQuery), streaming (Kafka, Kinesis), and data catalog/governance tools — used to enable a data mesh is highly preferred
- Working knowledge and experience with the data models within Siemens Teamcenter PLM solution – is highly preferred
- Knowledge of modern BI reporting/dashboard tools (Power BI, Tableau, or Looker) is highly preferred
- Working knowledge of AI/ML data readiness — including feature engineering, dataset versioning and provenance, vector stores, and curating data for retrieval-augmented generation (RAG) and predictive analytics use cases — is highly preferred
- Working knowledge of manufacturing operations, finance, supply chain, and other functional principles — and of the technology platforms (MES, ERP, PLM, QMS, historian) that constitute the Digital Thread — is highly preferred
Benefits
- Medical, dental, and vision coverage, along with prescription benefits
- 401(k) plan with company matching
- Flexible spending accounts
- Company-paid short- and long-term disability insurance
- Group life and accidental death and dismemberment insurance
- Paid vacation, paid sick leave, paid holidays, and paid parental leave
- Employee assistance program
- Fitness club membership contribution
- Pet insurance
- Identity theft protection
- Home and auto insurance discounts
- Optional supplemental life insurance
Company Overview