Metadata Management

Metadata Management Developer Jobs

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Today

Data Engineer (Data Management Support Officer) - SME

Bluehawk, LLC Reston, VA

Reston, VA

Today

Full time Analytics Business Analytics Business Intelligence

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Job Description

The Data Management Support Officer (Data Engineer) will act as a subject matter expert for UMM data strategy, governance, quality, interoperability, and performance measurement. Based in Reston, Virginia, this onsite role focuses on establishing unified data frameworks across CI and HUMINT executors and advising stakeholders on modernization priorities aligned to operational and enterprise objectives.

Responsibilities

Establish, test, evaluate, and maintain unified data frameworks, terminology, data models, and performance standards across CI and HUMINT executors.
Develop and enforce data-governance policies covering quality, security, access, compliance, and interoperability.
Oversee data input and output processes to ensure accurate executor representation and reliable operational information.
Maintain data dictionaries, metadata standards, taxonomies, and documentation that support enterprise interoperability.
Chair or coordinate data-governance working groups and enterprise forums with DoD and Intelligence Community stakeholders.
Implement data-quality monitoring, validation, cleansing, and continuous-improvement processes.
Facilitate cross-domain integration and data exchange across NIPR, SIPR, JWICS, and other approved environments.
Develop metrics, KPIs, performance-measurement frameworks, executive dashboards, and analytical products.
Analyze executor capability data to identify trends, gaps, anomalies, and optimization opportunities.
Advise ETO leadership on data modernization, resource allocation, operational planning, and enterprise interoperability.
Coordinate with tool administrators, data scientists, software engineers, and security personnel to align implementation with strategy.

Required Qualifications

Subject matter expertise in enterprise data management, data governance, data engineering, data architecture, or a related field.
Demonstrated experience establishing data frameworks, governance policies, metadata standards, data-quality controls, and performance measures.
Proficiency with analytical tools including Microsoft Excel, Tableau, Power BI, R, Python, or comparable platforms.
Strong understanding of CI/HUMINT operations, collection-management processes, or intelligence mission data.
Experience analyzing large datasets and developing metrics, KPIs, dashboards, reports, and executive recommendations.
Knowledge of IC and DoD data, security, information-sharing, and cross-domain requirements.
Excellent facilitation, advisory, briefing, and written and verbal communication skills.

Preferred Qualifications

Experience supporting DIA, UMM, enterprise source coordination, or comparable data-modernization programs.
Experience leading multidisciplinary teams or supervising data-platform and analytics personnel.

Technologies

Microsoft Excel
Tableau
Power BI
R
Python

Clearance and Work Requirements

Active TS/SCI CI Poly clearance and ability to work onsite at the DIA Government SCIF in Reston, Virginia during COR-established working hours.
Ability to comply with applicable DIA, DoD, Intelligence Community, cybersecurity, records-management, classification, and information-handling requirements.
Ability to work effectively in a non-personal services environment where contractor personnel remain accountable to contractor management.

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2mo ago

Associate Director, Data Engineer: DSCS Digital Data Strategy

Merck Boston, MA

Boston, MA

2mo ago Closed

$129k - $203k/yr Full time Senior Analytics Associate Director

This position is no longer accepting applications (closed Jul 28, 2026).

Job Description

Merck is seeking an Associate Director, Data Engineer within the DSCS Digital Data Strategy team, based on site in Boston, MA. The role centers on designing, constructing, and governing biologics data pipelines while shaping an end-to-end data strategy to support modeling, optimization, and decision-making across Digital Insights. This position requires a PhD and at least three years of relevant experience, with a salary range of $129,000 to $203,100 per year.

Responsibilities

Own the biologics data engineering domain, maintaining broad awareness of digital projects, data sources, systems, and data flows within the domain.
Contribute to defining and guiding the DSCS Digital Data Strategy.
Design and implement robust, scalable data pipelines that ingest experimental and process data from biologics source systems including process historians, chromatography equipment, electronic lab notebooks, and analytical instruments.
Provide analysis-ready datasets to support digital initiatives, covering process characterization models, data lineage, multivariate analytics, and cross-site manufacturing connectivity.
Establish and enforce data standards, metadata schemas, and ontology mappings to ensure biologics data is interoperable and readily usable by modeling and optimization workflows.
Collaborate proactively with automation teams to anticipate when new or modified automated workflows generate new data streams requiring pipeline development and ontology mapping.
Own and govern system-of-record standards for biologics, ensuring consistent configuration and data entry across experiments, molecules, and sites.
Catalog processes, analytical methods, instruments, and digital systems within the biologics domain to create a comprehensive data-landscape map.
Create and maintain data visualizations, dashboards, and reports that enable scientists to explore process data across runs, molecules, scales, and manufacturing sites.
Contribute to the biologics digital data strategy by identifying opportunities to improve data capture at the source and reduce friction between experimentation and modeling.
Mentor and guide supporting data engineers across modalities, ensuring alignment with domain strategy and ontology governance.
Maintain and version pipeline code in GitHub, adhering to team standards for code review, documentation, and deployment.
Build strong partnerships with process development scientists, analytical scientists, and manufacturing teams to gather requirements and shape the domain's digital data strategy.
Demonstrate excellent interpersonal, communication, and collaboration skills.
Model and promote diversity and inclusion within the team, fostering a supportive culture where all can thrive.
Collaborate effectively in a dynamic, integrated, multidisciplinary environment.

Requirements

Proficiency in Python and/or R, with comfort using development environments such as Jupyter, Posit/RStudio, or VS Code.
Strong SQL skills with hands-on experience writing and optimizing queries for relational databases and data warehouses.
Experience building ETL/ELT pipelines in a scientific or pharmaceutical context.
Familiarity with cloud platforms (AWS, Azure, or GCP) for data storage, processing, and integration.
Working knowledge of how process models, multivariate analyses, and statistical tools rely on experimental data to anticipate modeler needs and deliver structured datasets.
Experience defining or enforcing data standards, metadata schemas, or ontology mappings in a scientific or pharmaceutical setting.
Familiarity with version control systems (Git/GitHub) and collaborative software development practices.
Proven ability to lead technical initiatives, mentor junior engineers, and influence data strategy across multiple stakeholders.
Ability to deliver complex solutions under compressed timelines in a dynamic environment.

Technologies

Python and R
Jupyter, Posit/RStudio, VS Code
SQL
AWS, Azure, GCP
Databricks, Delta Lake
Git, GitHub
Streamlit, Shiny
Power BI, Spotfire, Tableau
Allotrope Simple Model, ISA-88, OPC-UA

Benefits

Medical, dental, and vision coverage for you and your family
Retirement benefits including a 401(k) plan
Paid holidays and vacation time
Sick leave and compassionate time off
Annual bonus and potential long-term incentives where applicable

Preferred Experience and Skills

Hands-on biologics process development experience—such as chromatography, filtration, purification, or formulation—and a track record transitioning into data engineering, data science, or computational roles.
Experience with data pipelines and analytics platforms such as Databricks, including notebook-based development, workflow orchestration, and Delta Lake.
Proven ability to create scientist-facing dashboards and exploratory data applications using visualization tools (Streamlit, Shiny, Power BI, Spotfire, Tableau).
Familiarity with ontology frameworks or standardized data models (e.g., Allotrope Simple Model, ISA-88, OPC-UA) and data mapping from instruments to structured schemas.
Understanding of Design of Experiments and process characterization study designs to structure data for statistical analysis of CPPs and CQAs.
Experience implementing data lineage and traceability across experimental systems, materials, and manufacturing steps.
Knowledge of regulatory expectations relevant to biologics process development, characterization, and validation (ICH Q8-Q12, validation lifecycle, comparability studies).
Experience collaborating with lab automation teams to integrate data from newly automated workflows.
Experience with cross-site data integration and harmonizing data from multiple manufacturing facilities.
Evidence of cross-functional collaboration spanning laboratory, manufacturing, modeling, and digital teams.

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Metadata Management Developer Jobs

Data Engineer (Data Management Support Officer) - SME

Associate Director, Data Engineer: DSCS Digital Data Strategy

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