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Key Responsibilities and Required Skills for Data Modeler

💰 $90,000 - $150,000

DataAnalyticsData ModelingData Engineering

🎯 Role Definition

The Data Modeler designs, builds, and maintains robust and scalable data models that support analytics, reporting and machine learning initiatives. Responsible for translating business requirements into conceptual, logical and physical models; optimizing schema design for performance in cloud and on-premise warehouses; enforcing modeling standards, metadata and lineage; and collaborating closely with data engineering, BI, ML and business stakeholders to deliver reliable, well-documented datasets. This role requires deep SQL skills, dimensional modeling expertise (star/snowflake), familiarity with ETL/ELT tools and cloud data platforms, and strong communication to align technical designs with business priorities.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Analyst transitioning to modeling responsibilities after working on complex reporting datasets and SQL design.
  • ETL/ELT Developer or Analytics Engineer with hands-on experience building pipelines and transforms (dbt, Airflow).
  • BI Developer who has been designing semantic models, reports, and enterprise dashboards.

Advancement To:

  • Senior Data Modeler / Lead Data Modeler responsible for architecture and standards across multiple domains.
  • Data Architect or Enterprise Data Architect leading data strategy, modeling frameworks and governance.
  • Head of Data Engineering or Principal Data Architect focusing on platform, governance and cross-functional strategy.

Lateral Moves:

  • Data Engineer focused on pipeline build and performance tuning in cloud data warehouses.
  • Analytics Engineer / dbt Developer building transformations and tests in CI/CD workflows.
  • Business Intelligence (BI) Architect working on semantic layers and reporting platforms.

Core Responsibilities

Primary Functions

  • Translate complex business requirements and analytics use cases into conceptual, logical, and physical data models that support self-service BI, operational reporting, and machine learning, documenting assumptions and trade-offs clearly for both technical and non-technical stakeholders.
  • Design dimensional models (star and snowflake schemas), normalized OLTP schemas, and hybrid approaches that optimize query performance, simplify reporting, and minimize data duplication while preserving data integrity.
  • Create and maintain enterprise-grade entity-relationship (ER) models, data dictionaries and metadata artifacts using modeling tools such as Erwin, ER/Studio, PowerDesigner or modern alternatives, ensuring model versioning and change tracking.
  • Collaborate with data engineers to implement physical models in cloud data warehouses (Snowflake, BigQuery, Redshift, Azure Synapse), including table structures, clustering/partitioning strategies, materialized views and distribution keys for optimal cost and performance.
  • Define and enforce data modeling standards, naming conventions, and best practices across domains (e.g., primary keys, surrogate keys, slowly changing dimensions, conformed dimensions) to ensure consistency and reusability of data assets.
  • Work with ETL/ELT teams to design efficient data ingestion and transformation pipelines, specifying source-to-target mappings, transformation logic, test cases and performance constraints for batch and streaming scenarios.
  • Optimize SQL and transformation logic to reduce query cost and runtime, including identifying expensive joins, rewriting queries, introducing aggregated tables or OLAP cubes, and advising on indexing and clustering strategies.
  • Design and implement Slowly Changing Dimensions (SCD Type 1/2/3) and other historical modeling patterns to preserve lineage and enable accurate time-based analysis for analytics and compliance.
  • Develop data lineage, impact analysis and model dependency diagrams to support change management, regulatory audits and data governance initiatives, ensuring traceability from source systems to analytics outputs.
  • Lead model validation and data reconciliation efforts with business owners, data stewards and QA teams to confirm accuracy of modeled datasets and surface data quality issues for remediation.
  • Partner with data governance, data privacy and security teams to classify data elements, define masking and access controls, and ensure models comply with regulatory requirements (GDPR, CCPA, HIPAA) and internal policies.
  • Support the design and implementation of master data and reference data models to enable Master Data Management (MDM) use cases and consistent lookup tables across the enterprise.
  • Collaborate with analytics, BI and product teams to design semantic layers and curated data marts that meet user needs for self-service analysis while preserving performance and data governance.
  • Build and maintain reusable modeling patterns, templates and accelerators to speed new domain onboarding and reduce technical debt across the data platform.
  • Provide guidance on physical design trade-offs for cloud cost optimization (storage vs compute), advising on table clustering, data pruning, compression, and partition lifecycle management.
  • Drive adoption of modeling automation tools and Infrastructure-as-Code (IaC) practices (dbt models, Terraform for data infra) to enable repeatable, testable deployment of data models in CI/CD pipelines.
  • Mentor junior modelers and data engineers on modeling techniques, documentation standards and troubleshooting strategies to improve team capability and cross-functional collaboration.
  • Assess legacy schemas and participate in data refactoring and migration projects, developing phased migration plans, compatibility layers and regression testing strategies to prevent downstream disruption.
  • Evaluate and recommend modeling approaches to support real-time analytics, event-driven architectures and streaming use cases, including schema design for time-series and high-velocity datasets.
  • Define metrics and KPIs for model health such as freshness, completeness, cardinality distributions and query performance, and set up monitoring/alerts to detect model degradation over time.
  • Lead workshops and requirement-gathering sessions with business stakeholders to elicit entities, relationships, reporting needs and edge cases that influence model design.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Create and maintain comprehensive data dictionaries, business glossaries and model documentation to accelerate user onboarding and reduce ambiguity in reporting.
  • Assist in vendor/tool evaluations for modeling, metadata management, lineage and data catalog solutions to improve discoverability and governance.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, performance tuning and query profiling for large datasets.
  • Conceptual, logical and physical data modeling: ER modeling, normalization, denormalization, and hybrid modeling techniques.
  • Dimensional modeling: star schema, snowflake schema, fact and dimension table design, conformed dimensions and grain definition (Kimball methodology).
  • Cloud data warehouses: hands-on experience with Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
  • ETL / ELT tooling and pipelines: dbt, Airflow, Informatica, Talend, Matillion, or custom Spark-based transformations.
  • Metadata management and data catalog tools: Collibra, Alation, Informatica EDC, or open-source equivalents; ability to maintain lineage and business glossary.
  • Modeling and diagramming tools: Erwin, ER/Studio, PowerDesigner, dbt docs; familiarity with version control for model artifacts (Git).
  • Data governance and security: data classification, masking, role-based access controls, and understanding of GDPR/CCPA/HIPAA implications for model design.
  • Performance optimization: partitioning, clustering, indexing, materialized views and aggregation strategies for fast analytics.
  • Data quality and testing: unit tests for models, reconciliation scripts, automated data validations and test-driven data modeling practices.
  • Master Data Management (MDM), Slowly Changing Dimensions (SCD), and surrogate key design patterns.
  • Programming and scripting: Python or SQL-based scripting for model automation, testing and orchestration.
  • BI and analytics platforms: familiarity with Tableau, Power BI, Looker, Qlik or equivalent to understand downstream consumption patterns.
  • Data lakehouse concepts and tools: lakehouse design, Parquet/ORC formats, Delta Lake, Apache Hudi for unified storage and querying.
  • Real-time and streaming schema design: experience with Kafka, Kinesis, or Pub/Sub and schema management (Avro/Protobuf/Schema Registry).
  • Data modeling for ML: feature store-friendly designs, support for training data sets, labeling metadata and reproducibility considerations.

Soft Skills

  • Strong stakeholder management and communication skills to align technical models with business priorities and explain trade-offs clearly.
  • Analytical problem solving and attention to detail to catch subtle data issues and design robust models that avoid ambiguity.
  • Collaboration and facilitation: run workshops, gather requirements and negotiate modeling decisions across cross-functional teams.
  • Documentation and teaching: ability to create clear documentation and train non-technical users on model use and limitations.
  • Time management and prioritization within agile development cycles and multiple concurrent modeling initiatives.
  • Adaptability to evolving platforms, new modeling patterns and rapidly changing business requirements.
  • Mentorship and leadership: guide junior team members and drive best-practice adoption across the data organization.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, or related technical field.

Preferred Education:

  • Master's degree in Data Science, Information Systems, Computer Science, Business Analytics, or a related discipline; professional certifications in data modeling, cloud platforms or data governance are a plus (e.g., SnowPro, Google Cloud Professional Data Engineer, dbt certification).

Relevant Fields of Study:

  • Computer Science
  • Information Systems
  • Data Science
  • Statistics
  • Mathematics
  • Software Engineering
  • Business Analytics

Experience Requirements

Typical Experience Range: 3 - 8 years of relevant experience in data modeling, data warehousing, or analytics engineering in an enterprise environment.

Preferred: 5+ years hands-on experience designing and implementing data models for BI/analytics on cloud data warehouses; demonstrated track record of translating complex business requirements into scalable, governed data models and working with cross-functional teams to deploy them into production.

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