Metadata Management

Metadata Management Jobs

3 jobs found

30d ago

Sr Analytics Engineer

Northwestern Mutual Milwaukee, WI

Milwaukee, WI

30d ago Closed

$119k - $178k/yr Full time Senior Analytics Artificial Intelligence

This position is no longer accepting applications (closed Sep 13, 2026).

Job Description

In the Business Intelligence Center of Excellence (BICOE) at Northwestern Mutual, this Sr Analytics Engineer role focuses on delivering reusable, governed analytical assets that support trusted enterprise decision-making and AI-enabled support.

Role Overview

You will help establish consistent cross-domain analytics by leading metric certification, lineage and metadata collection, and data quality monitoring. The position also emphasizes cataloging analytical products for enterprise visibility, promoting adoption and reuse, and ensuring alignment with enterprise data standards and governance practices.

Responsibilities

Lead business and technical requirements for developing reusable cross-domain analytical data products, with a focus on metric certification, lineage identification, and metadata collection.
Partner with business stakeholders to define, document, and align metrics, KPIs, business rules, and analytical requirements to support consistency, trust, and reuse.
Deliver a scalable analytics layer that standardizes enterprise metrics (OKRs/KPIs) to enable self-service and trusted cross-functional business intelligence.
Define analytical data quality rules that connect technical standards to business needs, proactively monitor and manage data drift, and support trusted analytical outcomes.
Conduct routine data validation, reconciliation, and quality assessments across the analytics lifecycle and BICOE-supported products.
Drive the cataloging of cross-domain analytical products to improve enterprise visibility and understanding of how to use assets in Atlan.
Act as an advocate for adoption and reuse of analytical data products and other enterprise approved assets.
Apply curiosity toward emerging technologies that improve data discoverability, contextual understanding, trusted consumption, and AI-enabled decision support.
Partner with Data Solutions & Enablement and business stakeholders to ensure end-to-end alignment with enterprise data standards, governance practices, and approved data sources.
Establish mentorship and coaching communities within and beyond the BICOE.

Required Qualifications

Bachelor’s Degree in a related field.
5+ years in analytics, data, or technology-related roles, including 3+ years applying the listed technical skills using platforms such as Databricks, Snowflake, AWS, Atlan, and related tools.
Demonstrated understanding of Large Language Models (LLMs), including prompt engineering, model capabilities, limitations, and practical business applications.
Ability to translate ambiguous business concepts into structured metrics, analytical models, and reusable data products.
Ability to work cross-functionally between business and technical stakeholders.
Experience with metric management platforms and strong collaboration in researching how metrics are sourced, calculated, and shared.
Strong aptitude for data quality, lineage, provenance, and governance in an analytics-focused environment.
Strong SQL skills, including query optimization and performance management.
Proven track record collecting, designing, and maintaining large data models.
Experience developing large analytical datasets, semantic models, dimensional models, or curated analytical data products.
Strong understanding of modern data modeling principles, including data grain, dimensions, facts, hierarchies, and reusable business logic.

Technologies

Databricks
Snowflake
AWS
Atlan
Large Language Models (LLMs)
Prompt engineering
SQL

Benefits

Flexible work schedules
Concierge service
Comprehensive benefits
Employee resource groups

Compensation

Annual pay range: USD 118,960 - 178,440
Geographic-specific pay structure:
- Structure 110: $130,880.00 USD - $196,320.00 USD
- Structure 115: $136,800.00 USD - $205,200.00 USD

Location and Work Style

Milwaukee, WI
Hybrid

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1mo ago

Technical Scrum/Data Analyst

Y&L Consulting San Antonio, TX

San Antonio, TX

1mo ago Closed

$70 - $75/hr Contract Agile Methodologies Agile Scrum Business Analytics

This position is no longer accepting applications (closed Sep 13, 2026).

Job Description

Y&L Consulting is hiring a Technical Scrum/Data Analyst for an onsite contract role in San Antonio, TX.

Responsibilities

Support large-scale data transformation programs and enterprise delivery efforts
Lead cross-functional work with business stakeholders, architects, engineers, and vendors
Provide project updates to PMO leadership, steering committees, and executive stakeholders
Manage Agile backlogs and delivery roadmaps for enterprise initiatives
Use Agile management tools such as Azure DevOps, Jira, or similar systems to track delivery

Requirements

5+ years of experience in Business Analysis, Data Analysis, Project Delivery, or related disciplines
5+ years serving as a Scrum Master or Agile Delivery Lead on enterprise technology initiatives
3+ years working with enterprise data platforms, data warehousing, or analytics solutions
Certified Scrum Master (CSM), SAFe Scrum Master, SAFe Agilist, or equivalent (preferred)
Strong understanding of Agile, Scrum, Kanban, and hybrid project methodologies
Strong understanding of Enterprise Data Hubs (EDH) and Master Data Management (MDM)
Demonstrated knowledge across enterprise data governance topics:
- Data Quality Management
- Data Lineage
- Metadata Management
- Data & Metric Catalog
Experience with data integration concepts including ETL and ELT
Knowledge of data warehousing methodologies
Hands-on familiarity with at least one cloud data platform/tooling set:
- Snowflake, Databricks, Google BigQuery
- Azure Data Platform, AWS Data Services, Google Data Platform
SQL and data analysis tools

Technology Focus

Enterprise Data Hubs (EDH) and Master Data Management (MDM)
Data Quality Management, Data Lineage, Metadata Management
Data & Metric Catalog
Data Integration, ETL/ELT, Data Warehousing methodologies
Snowflake, Databricks, Google BigQuery
Azure Data Platform, AWS Data Services, Google Data Platform
SQL
Azure DevOps, Jira
Informatica MDM
Cloud-based data lakehouse architectures

Preferred Qualifications

Experience implementing or supporting Master Data Management using Informatica MDM
Experience in enterprise data modernization initiatives
Knowledge of cloud-based data lakehouse architectures
Experience supporting data governance councils and data stewardship programs
Familiarity with data catalog and metadata management tools

Job Details

Location: San Antonio, TX (onsite)
Contract role
Pay: USD 70 - 75 per hour
Minimum experience: 3 years

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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

The role of Associate Director, Data Engineer within DSCS Digital Data Strategy is based onsite in Boston, MA. It leads the design, construction, and governance of biologics data pipelines and defines end-to-end data strategy to enable modeling, optimization, and decision support across Digital Insights. A Ph.D. is required, and the compensation ranges from USD 129,000 to 203,100 per year.

Responsibilities

Act as the domain owner for biologics data engineering, maintaining a comprehensive view of all digital projects, data sources, systems, and data flows within the domain.
Contribute to shaping the DSCS Digital Data Strategy by informing relevant work and initiatives.
Design and build robust, scalable data pipelines that ingest experimental and process data from biologics source systems such as process historians, chromatography systems, electronic lab notebooks, and analytical instruments.
Provide analysis-ready datasets to support digital initiatives, including process characterization models, data lineage, multivariate analytics, and cross-site manufacturing connectivity.
Define and enforce data standards, metadata schemas, and ontology mappings to ensure biologics data is interoperable for modeling and optimization workflows.
Collaborate with automation colleagues, anticipating when new or modified automated workflows create new data streams that require pipeline development and ontology mapping.
Own and govern system of record standards for biologics, ensuring consistent configuration and data entry practices across experiments, molecules, and sites.
Catalog all processes, analytical methods, instruments, and digital systems within the biologics domain to create a comprehensive data landscape map.
Develop and maintain data visualizations, dashboards, and reports enabling scientists to explore process data across runs, molecules, scales, and manufacturing sites.
Influence the digital data strategy for biologics 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 all pipeline code in GitHub, following 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 strong interpersonal, communication, and collaboration skills.
Model and promote the core values of diversity and inclusion, fostering a supportive culture where all can thrive.
Collaborate effectively in a dynamic, integrated, and multidisciplinary team environment.

Requirements

Proficiency in Python and/or R, with comfort using development environments such as Jupyter, Posit/RStudio, or VS Code.
Solid SQL skills with hands-on experience writing and optimizing queries against relational databases and data warehouses.
Experience with ETL/ELT processes and building data 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 consume 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 context.
Familiarity with version control systems (Git/GitHub) and collaborative software development practices.
Demonstrated 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
R
Jupyter
Posit/RStudio
VS Code
SQL
AWS
Azure
GCP
Databricks
Delta Lake
Git
GitHub
Streamlit
Shiny
PowerBI
Spotfire
Tableau
Allotrope Simple Model
ISA-88
OPC-UA

Benefits

Medical, dental, and vision coverage for employee and family
Retirement benefits including a 401(k) plan
Paid holidays
Vacation time
Compassionate and sick leave
Annual bonus and long-term incentive, if applicable

Preferred Experience and Skills

Hands-on biologics process development experience such as chromatography, filtration, purification, or formulation, transitioning into a data engineering, data science, or computational role.
Experience with Databricks, including notebook-based development, workflow orchestration, and Delta Lake.
Experience with data visualization tools (Streamlit, Shiny, PowerBI, Spotfire, Tableau) for scientist-facing dashboards and exploratory apps.
Familiarity with ontology frameworks or standardized data models (eg, Allotrope Simple Model, ISA-88, OPC-UA) and mapping instrument data to structured schemas.
Understanding of Design of Experiments and process characterization study designs to structure data for statistical analysis of CPPs and CQAs.
Experience with data lineage and building traceability across experimental systems, materials, and manufacturing steps.
Knowledge of regulatory expectations relevant to biologics process development, process characterization, and process validation (ICH Q8-Q12, validation lifecycle, comparability studies).
Experience collaborating with lab automation teams and integrating data from newly automated workflows.
Experience with cross-site data integration, harmonizing data from multiple facilities with different systems and conventions.
Evidence of cross-functional collaborations spanning laboratory, manufacturing, modeling, and digital teams.

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Metadata Management Jobs

Sr Analytics Engineer

Technical Scrum/Data Analyst

Associate Director, Data Engineer: DSCS Digital Data Strategy

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