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Analytics Engineer Job Description

1. Job Summary

The Analytics Engineer is responsible for designing, developing, and maintaining scalable data pipelines, analytics infrastructure, and business-ready data models that support data-driven decision-making across the organization. The role bridges data engineering and business intelligence by transforming raw data into reliable, high-quality datasets that enable reporting, analytics, and strategic insights.

The successful candidate will collaborate with Product, Finance, Operations, Go-to-Market (GTM), and other business teams to understand analytical requirements, optimize data architecture, improve data quality, and deliver scalable analytics solutions. The role also involves mentoring junior team members, promoting engineering best practices, and ensuring the reliability, performance, and governance of the organization's data ecosystem.

2. Key Duties and Responsibilities

2.1 Data Pipeline Development

The successful candidate will:

• Design, develop, and maintain scalable data pipelines.

• Build reliable Extract, Transform, and Load (ETL/ELT) workflows.

• Integrate data from internal and external sources.

• Optimize data ingestion processes for efficiency and scalability.

• Ensure data pipelines operate reliably across cloud and on-premise environments.

• Continuously improve pipeline performance and reliability.

2.2 Data Modeling and Transformation

The Analytics Engineer will:

• Develop analytical data models that support business reporting.

• Transform raw data into clean, business-ready datasets.

• Design semantic layers for consistent business reporting.

• Maintain scalable and reusable data models.

• Ensure data structures support current and future business needs.

• Apply best practices in data modeling and transformation.

2.3 Data Quality and Governance

The employee will:

• Monitor data quality across reporting systems.

• Identify and resolve data inconsistencies.

• Implement validation and monitoring processes.

• Improve data governance and documentation.

• Maintain data accuracy, integrity, and reliability.

• Support compliance with organizational data standards.

2.4 Business Intelligence and Analytics Support

The successful candidate will:

• Partner with Product, Operations, Finance, Sales, and GTM teams to understand reporting requirements.

• Deliver business insights through reliable datasets and dashboards.

• Support business intelligence initiatives.

• Assist stakeholders in interpreting analytical results.

• Develop reporting solutions that support strategic decision-making.

• Recommend improvements based on data analysis.

2.5 Data Pipeline Optimization

The Analytics Engineer will:

• Optimize ETL/ELT workflows for performance and cost efficiency.

• Improve orchestration of automated data pipelines.

• Monitor system performance and resource utilization.

• Enhance scalability of analytics infrastructure.

• Troubleshoot data processing issues.

• Recommend architecture improvements that support long-term growth.

2.6 Reporting and Dashboard Management

The employee will:

• Develop and maintain reporting datasets.

• Support business intelligence tools and dashboards.

• Ensure reporting accuracy and consistency.

• Monitor reporting system health.

• Improve reporting automation.

• Maintain documentation for reporting processes.

2.7 Cloud and Data Platform Administration

The successful candidate will:

• Support cloud-based data warehouse environments.

• Manage storage, processing, and data integration platforms.

• Monitor system performance and availability.

• Assist in maintaining secure and reliable data infrastructure.

• Support cloud optimization initiatives.

• Ensure efficient utilization of cloud resources.

2.8 Incident Management and System Reliability

The Analytics Engineer will:

• Participate in incident response activities.

• Investigate and resolve data-related issues.

• Conduct root cause analysis following incidents.

• Implement preventive measures that improve system reliability.

• Support on-call rotations where required.

• Document incidents and corrective actions.

2.9 Documentation and Best Practices

The employee will:

• Maintain technical documentation for data pipelines and systems.

• Develop data engineering standards and procedures.

• Document business metrics and analytical models.

• Promote coding standards and best practices.

• Support continuous process improvement initiatives.

• Ensure knowledge sharing across the team.

2.10 Team Collaboration and Mentorship

The successful candidate will:

• Mentor junior analytics and data engineering team members.

• Collaborate with cross-functional technical and business teams.

• Participate in technical planning and project discussions.

• Share technical knowledge and best practices.

• Support continuous learning within the department.

• Perform any other duties assigned by management.

3. Minimum Qualifications

Applicants should possess the following qualifications:

• Bachelor's Degree in Computer Science, Data Science, Information Technology, Software Engineering, Statistics, Mathematics, Engineering, or a related quantitative field from a recognized institution.

• Professional certifications in Data Engineering, Cloud Computing, Business Intelligence, or Analytics will be an added advantage.

4. Work Experience

The ideal candidate should have:

• A minimum of three (3) to five (5) years of experience in Analytics Engineering, Data Engineering, or a related role.

• Proven experience designing and maintaining scalable data pipelines.

• Practical experience with data modeling and transformation frameworks.

• Experience working with cloud-based data platforms and modern data warehouses.

• Experience developing business intelligence and reporting solutions.

• Experience integrating multiple data sources through APIs and ETL/ELT processes.

• Experience supporting analytics infrastructure in production environments.

5. Required Skills and Competencies

The successful candidate should demonstrate the following skills:

• Advanced SQL programming and query optimization.

• Strong Python programming skills.

• Experience using dbt for data transformation and modeling.

• Knowledge of analytical data modeling techniques.

• Experience with batch and streaming data processing.

• Familiarity with modern data warehouse technologies.

• Experience with ETL/ELT tools and workflow automation.

• Knowledge of cloud platforms, particularly Amazon Web Services (AWS).

• Experience with business intelligence and reporting tools.

• Version control using Git and CI/CD practices.

• Strong analytical and problem-solving skills.

• Excellent documentation and technical writing abilities.

• Strong communication and stakeholder management skills.

• Ability to explain technical concepts to non-technical audiences.

• Excellent organizational and project management skills.

6. Personal Attributes

The ideal candidate should possess the following qualities:

• Strong analytical mindset.

• High attention to detail.

• Customer-focused approach.

• Innovative and solution-oriented thinking.

• Strong sense of ownership and accountability.

• Ability to work independently with minimal supervision.

• Adaptability in a fast-paced environment.

• Commitment to continuous improvement.

• Excellent teamwork and collaboration skills.

• Professionalism and integrity.

7. Working Environment

The Analytics Engineer will primarily work in a hybrid or remote technology environment, collaborating with engineering teams and business stakeholders across multiple departments. The role involves extensive use of cloud platforms, data warehouses, analytics tools, and software development technologies. The successful candidate may participate in on-call support rotations, incident response activities, and cross-functional projects while managing multiple priorities in a dynamic, technology-driven environment.

8. Key Performance Indicators (KPIs)

Performance in this role will be measured using the following indicators:

• Reliability and availability of data pipelines.

• Accuracy and quality of business-ready datasets.

• Timely delivery of analytics solutions.

• Data pipeline performance and optimization.

• Reduction in data quality issues.

• Reporting accuracy and consistency.

• Cloud infrastructure efficiency and cost optimization.

• Successful completion of data engineering projects.

• Compliance with documentation and engineering standards.

• Incident response effectiveness and system reliability.

• Stakeholder satisfaction with analytics solutions.

• Contribution to mentoring and team development.

9. Expected Outcomes

The successful Analytics Engineer will contribute to the organization by:

• Building reliable and scalable analytics infrastructure that supports business growth.

• Delivering accurate, high-quality datasets for reporting and decision-making.

• Improving the efficiency and reliability of data pipelines and engineering processes.

• Supporting business teams with actionable insights through robust analytics solutions.

• Strengthening data governance, monitoring, and documentation practices.

• Optimizing cloud resources and reducing operational costs.

• Enhancing reporting capabilities through effective data modeling.

• Supporting continuous innovation in analytics engineering practices.

• Developing technical capabilities within the team through mentoring and collaboration.

• Promoting a culture of data quality, operational excellence, and continuous improvement.

The Analytics Engineer is expected to perform all assigned responsibilities with professionalism, technical excellence, integrity, accountability, and a strong commitment to delivering scalable, reliable, and high-quality analytics solutions that support the organization's strategic objectives.

Application

Go to CloudFactory on jobs.workable.com to apply