Analytics Engineer – Data Modeling and Transformation

The Elite Job; Cheshire (CT); On-site; USD 90,000 - 130,000; Full time

Job summary

The Elite Job is seeking a skilled Analytics Engineer – Data Modeling and Transformation to join our growing data and technology team. This role focuses on turning raw data into structured datasets that support business intelligence, reporting, and decision-making.

You will design scalable data models, build efficient transformation pipelines (dbt and related frameworks), ensure data quality, and collaborate with data engineers, BI developers, product teams, and stakeholders to translate

Qualifications

• Strong proficiency in SQL, including advanced querying, joins, aggregations, window functions, and optimization.
• Solid understanding of relational databases, data warehouses, data marts, and analytical data structures.
• Knowledge of dimensional modeling concepts including fact and dimension tables, star/snowflake schemas.
• Experience developing data transformation workflows and preparing datasets for analytics and reporting.
• Familiarity with modern data transformation tools such as dbt.
• Understanding of ETL and ELT processes and cloud data platforms such as Snowflake, BigQuery, Redshift, Databricks.
• Familiarity with Python for data processing, validation, or automation.
• Knowledge of Git and collaborative development practices.
• Understanding of data quality testing, validation, and data governance.

Responsibilities

• Data Modeling and Architecture: design and maintain data models supporting reporting and analytics.
• Data Transformation: build reliable workflows to convert raw data into clean, analysis-ready datasets.
• SQL Development: write and optimize complex SQL queries and transformations.
• Analytics Engineering Frameworks: use dbt or similar to organize models and manage dependencies.
• Data Pipeline Development: integrate data from databases, APIs, and cloud storage into analytic platforms.
• Data Quality Management: develop validation rules and automated tests for data quality.

Skills

SQL proficiency

Data modeling

Analytical thinking

Problem solving

Communication

Education

Bachelor's degree in CS/IT/Data Science or equivalent

Tools

dbt

Python

Snowflake/BigQuery/Redshift

Job description

Job Summary

The Elite Job is seeking a skilled and analytical Analytics Engineer – Data Modeling and Transformation to join our growing data and technology team. This role is ideal for a professional who enjoys transforming complex, raw data into reliable, structured, and meaningful datasets that support business intelligence, reporting, and strategic decision-making.

As an Analytics Engineer, you will bridge the gap between data engineering and data analytics by designing scalable data models, developing efficient transformation pipelines, maintaining data quality, and ensuring that business users have access to accurate and well-organized information. You will work closely with data engineers, data analysts, business intelligence developers, product teams, and business stakeholders to translate business requirements into dependable analytical solutions.

The successful candidate will have a strong understanding of SQL, data modeling principles, data transformation frameworks, and modern cloud-based data platforms. You will contribute to building trusted data assets that improve reporting accuracy, operational efficiency, and organizational performance.

At The Elite Job, we value problem-solving, continuous improvement, collaboration, and a commitment to delivering high-quality data solutions that create measurable business value.

Key Responsibilities

• Data Modeling and Architecture: Design, develop, and maintain logical and physical data models that support business reporting, analytical workloads, and decision-making processes.
• Data Transformation: Build and maintain reliable data transformation workflows that convert raw source data into clean, structured, and analysis-ready datasets.
• SQL Development: Write, optimize, and maintain complex SQL queries, common table expressions, joins, aggregations, and analytical transformations.
• Analytics Engineering Frameworks: Use tools such as dbt or similar transformation frameworks to organize data models, implement reusable logic, and manage dependencies between datasets.
• Data Pipeline Development: Collaborate with data engineers to integrate information from databases, APIs, cloud storage, and other business systems into analytical data platforms.
• Data Quality Management: Develop validation rules, reconciliation checks, and automated tests to identify missing values, duplicate records, inconsistencies, and transformation errors.
• Data Warehouse Optimization: Work with modern data warehouses to improve query performance, data accessibility, storage efficiency, and processing reliability.
• Business Intelligence Support: Prepare trusted datasets for dashboards, reports, key performance indicators, and self-service analytics applications.
• Stakeholder Collaboration: Work with business analysts, finance teams, operations teams, product managers, and other stakeholders to understand analytical requirements and deliver appropriate data solutions.
• Documentation: Maintain clear documentation covering data models, transformation logic, source systems, business definitions, dependencies, and data lineage.
• Performance Monitoring: Monitor transformation jobs and data workflows, investigate failures, resolve issues, and implement preventive measures to improve reliability.
• Version Control: Use Git or equivalent version-control systems to manage code changes, review contributions, and maintain a structured development process.
• Workflow Automation: Identify opportunities to automate repetitive data preparation tasks and reduce manual intervention in reporting processes.
• Governance and Security: Follow organizational standards for data access, privacy, security, retention, and responsible handling of sensitive information.
• Continuous Improvement: Evaluate emerging analytics engineering practices, tools, and technologies to improve the scalability, maintainability, and effectiveness of data solutions.

Required Skills and Qualifications

• Bachelors degree in Computer Science, Information Technology, Data Science, Statistics, Engineering, or a related discipline, or equivalent practical experience.
• Strong proficiency in SQL, including advanced querying, joins, aggregations, window functions, and query optimization.
• Solid understanding of relational databases, data warehouses, data marts, and analytical data structures.
• Knowledge of dimensional modeling concepts, including fact tables, dimension tables, star schemas, and snowflake schemas.
• Experience developing data transformation workflows and preparing datasets for analytics and reporting.
• Familiarity with modern data transformation tools such as dbt or equivalent technologies.
• Understanding of ETL and ELT processes and the differences between traditional data integration and warehouse-based transformations.
• Experience working with data platforms such as Snowflake, Google BigQuery, Amazon Redshift, Databricks, or comparable technologies.
• Familiarity with programming languages such as Python for data processing, validation, or automation.
• Knowledge of Git, branching strategies, pull requests, and collaborative development practices.
• Understanding of data quality testing, validation frameworks, data reconciliation, and error handling.
• Ability to interpret business requirements and convert them into well-defined data models and transformation logic.
• Strong analytical thinking, troubleshooting, and problem-solving abilities.
• Excellent written and verbal communication skills, with the ability to explain technical concepts to non-technical stakeholders.
• Strong organizational skills and attention to detail when managing complex datasets and multiple priorities.

Experience

• Preferred Experience: 2–5 years of experience in analytics engineering, data engineering, business intelligence, data analytics, or a closely related technical role.
• Demonstrated experience writing SQL queries and building reliable data transformation workflows.
• Practical experience designing analytical data models for reporting, dashboarding, or business performance analysis.
• Experience working with cloud data warehouses or enterprise database environments.
• Familiarity with automated testing, data quality checks, and version-controlled development workflows.
• Experience collaborating with analysts, engineers, and business teams to deliver data-driven solutions.
• Exposure to orchestration tools such as Apache Airflow, Dagster, Prefect, or equivalent platforms is an advantage.
• Experience with BI tools such as Power BI, Tableau, or Looker is beneficial.
• Familiarity with CI/CD practices, cloud platforms, and data governance standards is desirable.
• Candidates with strong SQL fundamentals, relevant projects, internships, or demonstrable technical skills may also be considered for suitable entry-level opportunities, depending on business requirements.

Working Hours

Employment Schedule: Full-time, generally 40 hours per week.

Typical Working Hours: Monday to Friday, with schedules determined by the assigned team and business requirements.

Work Flexibility: Flexible scheduling may be available depending on the role, project commitments, and team policies.

Remote Collaboration: Employees working remotely may be required to attend virtual meetings, planning sessions, and technical discussions.

Time Zone Coordination: Some overlap with international colleagues or business stakeholders may be required.

Project Deadlines: Occasional adjustments to working hours may be necessary during critical releases, data migrations, or production incidents, in accordance with company policies.

Final working hours, employment conditions, and remote-work arrangements will be confirmed during the recruitment process.

Knowledge, Skills, and Abilities

Technical Knowledge

• Strong understanding of SQL-based data manipulation and transformation.
• Knowledge of data warehouse architecture, dimensional modeling, and analytical database design.
• Understanding of data integration patterns, including ETL, ELT, incremental loading, and data refresh strategies.
• Familiarity with transformation dependencies, reusable data models, and modular analytics development.
• Awareness of data observability, data lineage, schema changes, and data quality monitoring.
• Understanding of query execution plans, indexing concepts where applicable, and warehouse performance optimization.

Analytical Skills

• Ability to investigate complex data problems and identify their underlying causes.
• Capacity to evaluate data relationships and develop models that reflect business processes accurately.
• Strong attention to detail when validating calculations, business rules, and reporting metrics.
• Ability to identify trends, inconsistencies, and opportunities to improve data reliability.

Communication and Collaboration

• Ability to work effectively with technical and non-technical stakeholders.
• Clear documentation and presentation skills.
• Willingness to participate in code reviews, share knowledge, and contribute to team standards.
• Ability to manage competing priorities and communicate progress, risks, and delivery timelines.

Professional Abilities

• A proactive approach to identifying and resolving data-related issues.
• Commitment to data accuracy, security, and maintainable code.
• Adaptability when working with new tools, platforms, and business requirements.
• A continuous-learning mindset and interest in modern data technologies.
• Accountability for the quality, reliability, and usability of delivered data assets.

Benefits

• Competitive Compensation: Salary packages aligned with relevant skills, experience, responsibilities, and market conditions.
• Flexible Work Arrangements: Remote or hybrid working options where supported by the position and business requirements.
• Professional Development: Opportunities to strengthen expertise in analytics engineering, cloud technologies, data modeling, and business intelligence.
• Technical Learning: Exposure to modern data platforms, transformation frameworks, and data engineering best practices.
• Collaborative Environment: Opportunities to work with professionals across data engineering, analytics, product, and business functions.
• Career Advancement: Potential progression into senior analytics engineering, data architecture, data platform engineering, or technical leadership roles.
• Knowledge Sharing: Opportunities to participate in technical discussions, peer reviews, and continuous improvement initiatives.
• Work-Life Balance: Support for sustainable working practices, subject to project commitments and company policies.
• Recognition and Growth: Opportunities to demonstrate technical ownership, contribute to business outcomes, and expand professional responsibilities.
• Specific benefits, allowances, insurance coverage, paid leave, and other employment provisions will be communicated as part of the formal offer where applicable.

Why Join The Elite Job?

Joining The Elite Job offers an opportunity to contribute to data-driven business transformation while developing valuable technical and professional skills.

As an Analytics Engineer, you will help establish the trusted data foundations that organizations need to make informed decisions. Your work will support reliable reporting, consistent business metrics, improved operational visibility, and more efficient analytical processes.

You will have the opportunity to:

• Make a Meaningful Impact: Develop data models and transformation workflows that help teams access accurate, actionable information.
• Build Technical Expertise: Strengthen your knowledge of SQL, data warehouses, analytics engineering frameworks, and cloud-based data platforms.
• Solve Challenging Problems: Work on data quality, performance optimization, transformation design, and analytical data reliability.
• Collaborate Across Teams: Partner with data engineers, business intelligence specialists, analysts, and business stakeholders.
• Grow Your Career: Develop a foundation for advanced roles in data engineering, analytics architecture, and technical leadership.
• Improve Data Practices: Contribute to reusable models, automated testing, consistent definitions, and scalable analytics processes.
• Embrace Continuous Learning: Stay informed about evolving data technologies and modern engineering practices.

We welcome professionals who combine technical ability with curiosity, accountability, and a commitment to creating dependable data solutions.