Senior Data Engineer

Position Summary This position is responsible for managing and organizing data to support all business processes to achieve corporate and departmental goals. This position is responsible for identifying trends and communicating these trends clearly to others in the organization to ensure data is properly used. Core activities include troubleshooting data issues, and assisting the data architect to develop, align, and maintain architectures with business requirements. In addition, this position is expected to implement strategies to acquire high quality data, timely, accurately, reliably, and efficiently by developing the right data processes with the applications and integration engineers to meet all necessary compliance and governance needs. Duties and 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. Experience and Qualifications Bachelor's degree in Computer Science, Software Engineering, or other related science or engineering discipline is required. Advanced degree preferred. A minimum of four years experience in Data Modeling, Data Solution Development, and Data Integration. Experience integrating data from manufacturing and operational systems (MES, ERP, PLM, SCADA/historian, LIMS, or QMS) is highly preferred. 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; 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, Skills and Abilities Demonstrated ability to manipulate and aggregate structured and unstructured data from multiple sources by constructing complex queries for analysis and reporting. Excellent communication and interpersonal skills, with the ability to convey technical issues, tradeoffs, and results to technical and business stakeholders. Strong ownership and delivery orientation - proactive, detail‑oriented, and able to manage multiple priorities against time‑sensitive deadlines. 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. Understanding of the configuration elements and integration methods and plugin capabilities of enterprise tools. Physical Demands This position requires the ability to communicate and exchange information, utilize equipment necessary to perform the job, and move about the office. Work Environment This position is performed in a traditional office environment, or a hybrid model. Compensation Summary The annual base salary for this position ranges from $101,400.00 to $154,650.00. This salary range represents a general guideline as MSD considers other factors when presenting an offer of employment, such as scope and responsibilities of the position, external market factors, and the candidate's knowledge, skills, abilities, education and experience. Employees may qualify for a discretionary or non‑discretionary bonus in addition to their base salary. These annual bonuses are intended to recognize individual performance and enable employees to benefit from the Company's overall success. Benefits Summary At MSD, we offer a comprehensive benefits package to support our employees' well‑being and financial security. In addition to competitive salaries, our benefits include medical, dental, and vision coverage, along with prescription benefits. We provide a 401(k) plan with company matching, flexible spending accounts, and company‑paid short‑ and long‑term disability insurance as well as group life and accidental death and dismemberment insurance. Our offerings also encompass paid vacation, paid sick leave, paid holidays, and paid parental leave, along with an employee assistance program. Additional voluntary perks include a fitness club membership contribution, pet insurance, identity theft protection, home and auto insurance discounts, and optional supplemental life insurance. EEO/AA Statement MSD is an Equal Opportunity/Affirmative Action Employer. We are committed to fostering a diverse and inclusive workplace where all individuals are treated with respect and dignity. We welcome applications from all qualified candidates, making employment decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, genetic information, marital status, national origin, age, protected veteran status, pregnancy, disability status, or any other protected characteristic. For our full EEO/AA and Pay Transparency statement, please visit. Meso Scale Diagnostics uses E-Verify to validate the work eligibility of candidates. #J-18808-Ljbffr Meso Scale Discovery