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Senior Analytics Engineer

icon building Company : Sabenza It
icon briefcase Job Type : Full Time

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Job Description - Senior Analytics Engineer

Role Purpose:

The Senior Analytics Engineer is responsible for designing
and leading the development of scalable, high -quality analytics data platforms
that enable advanced analytics, business intelligence, and AI use cases. This
role plays a strategic function in defining data modelling standards, ensuring
data reliability, and aligning analytics engineering practices with
organisational data strategy. The role also provides technical leadership and
mentorship within the analytics engineering function, ensuring best practices,
consistency, and continuous improvement across the data lifecycle.


Requirements

Minimum

  • Bachelor’s degree in Data Science, Statistics, Computer
    Science, Information Systems, Engineering, or related field.
  • 5+ years of experience in analytics engineering, data
    engineering, or advanced analytics roles.
  • Expert -level proficiency in SQL and extensive experience
    designing scalable analytical data models.
  • Strong experience with data transformation tools (e.g., dbt)
    and modern data warehouses (e.g., BigQuery, Snowflake, Redshift).
  • Deep understanding of data modelling concepts (star schema,
    dimensional modelling, data vault, fact/dimension design).
  • Proven experience working with BI tools (Power BI, Tableau,
    Looker etc.) and enabling self -service analytics.
  • Strong experience with version control systems (e.g., Git)
    and CI/CD practices in data workflows.

Responsibilities

Analytics Data Modelling & Architecture

  • Lead the design, development, and optimisation of scalable
    analytics data models and data layers.
  • Define and enforce modelling standards, best practices, and
    architectural patterns across the organisation.
  • Ensure consistent, reusable, and well -governed definitions
    of metrics, dimensions, and business logic.

Data Quality, Reliability & Observability

  • Establish and oversee robust data quality frameworks,
    testing strategies, and monitoring systems.
  • Ensure reliability and performance of data pipelines and
    analytical models at scale.
  • Drive root cause analysis and resolution of complex data
    issues across upstream and downstream systems.

Enablement of Advanced Analytics & BI

  • Partner with data scientists, analysts, and BI teams to
    enable advanced analytics and AI use cases.
  • Design data models that support scalability, performance,
    and flexibility for diverse analytical needs.
  • Champion self -service analytics by creating well -structured,
    accessible, and performant datasets.

Leadership, Governance & Standards

  • Define and implement analytics engineering standards,
    governance frameworks, and best practices.
  • Lead documentation initiatives for data models, definitions,
    and lineage.
  • Ensure compliance with data governance, security, and
    regulatory requirements.

Stakeholder Engagement & Strategy

  • Collaborate with business and technical stakeholders to
    align data models with strategic objectives.
  • Translate complex business requirements into scalable and
    maintainable data solutions.
  • Contribute to data platform strategy, roadmap, and tooling
    decisions.

Mentorship & Team Development

  • Mentor and guide junior and mid -level analytics engineers.
  • Conduct code reviews and provide technical oversight.
  • Foster a culture of continuous learning, collaboration, and
    excellence.

Role Competencies

Technical

  • Expert -level SQL and advanced data modelling expertise.
  • Deep experience with modern data stack tools and
    architectures.
  • Strong understanding of data pipelines, orchestration, and
    dependencies.
  • Familiarity with software engineering best practices in data
    (CI/CD, testing, modular design).

Analytical & Problem -Solving

  • Ability to design scalable solutions to complex data
    challenges.
  • Strong critical thinking and data validation skills.
  • Proactively identifies risks, inefficiencies, and
    improvement opportunities.

Communication & Collaboration

  • Strong stakeholder management and communication skills.
  • Ability to translate technical concepts into
    business -friendly insights.
  • Produces high -quality documentation and promotes knowledge
    sharing.

Leadership & Influence

  • Demonstrates technical leadership and decision -making
    capability.
  • Influences data strategy and promotes best practices across
    teams.
  • Builds alignment across cross -functional teams.

Adaptability and Agility       

  • Thrives in dynamic, fast -paced environments.
  • Continuously evolves tools, practices, and skillsets.
  • Drives innovation in analytics engineering approaches.


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