Senior Analytics Engineers / Mid Analytics Engineers

# Think Data Be Smart #

About Us: DataSmart is a Portuguese company, positioning itself as a consulting company of excellence, with over 20 years of existence. We are specialized in Technologies and Information Systems services, for the Portuguese and International markets. We pride ourselves on fostering a culture of involvement, experience, and excellence.

Join our team as a Senior Analytics Engineers / Mid Analytics Engineers (Hybrid – Lisbon)!

What You’ll Do:.

• Design and implement scalable analytical data models using Star Schema, Dimensional Modeling, and Medallion Architecture.
• Create semantic layers that enable consistent reporting, analytics, and machine learning consumption.
• Manage schema evolution while maintaining data consistency and governance standards.
• Optimize large-scale analytical models for performance and maintainability.
• Develop and maintain ELT/ETL pipelines to transform raw data into trusted analytical datasets.
• Build advanced data transformations using SQL, Python, PySpark, and Spark SQL.
• Automate data processing workflows and improve pipeline reliability.
• Ensure data is structured and accessible for BI, Data Science, and ML teams.
• Implement and manage orchestration workflows using tools such as Airflow, Azure Data Factory, Databricks Workflows, or Microsoft Fabric.
• Automate scheduling, dependency management, and recovery processes.
• Improve operational efficiency through workflow optimization and automation.
• Define and implement data quality frameworks and validation rules.
• Monitor data quality, completeness, accuracy, freshness, and SLA compliance.
• Establish end-to-end observability and alerting mechanisms.
• Identify and resolve data issues before they impact consumers.
• Optimize SQL queries and analytical workloads.
• Implement partitioning, clustering, and performance-tuning strategies.
• Improve processing efficiency for large-scale datasets.
• Ensure analytical models and semantic layers perform efficiently under growing demand.
• Document business rules, transformation logic, and data lineage.
• Support governance initiatives using tools such as Purview or Unity Catalog.
• Maintain traceability of data across source systems and analytical layers.
• Ensure compliance with organizational data standards.
• Design and optimize semantic models for Power BI and other BI platforms.
• Improve DAX calculations and reporting performance.
• Enable self-service analytics through well-structured business-friendly data models.
• Implement CI/CD pipelines and DevOps practices for data products.
• Apply version control and automated testing to data pipelines.
• Promote coding standards, peer reviews, and deployment automation.
• Contribute to a reliable and scalable analytics engineering ecosystem.
• Partner with Data Engineers, Data Scientists, ML Engineers, Product Owners, and Business stakeholders.
• Translate business requirements into technical data solutions.
• Ensure analytical solutions align with business goals and decision-making needs.
• Support integrated, value-driven delivery across teams.

Qualifications:

• Understanding of business objectives and key performance indicators.
• Reporting, analytics, and machine learning use cases.
• Business requirements, priorities, and expected outcomes.
• Clear definition of success criteria for analytical products.
• Access to source systems, databases, APIs, and data feeds.
• Data dictionaries and source system documentation.
• Existing analytical models and data flows.
• Understanding of data ownership and stewardship.
• Access to cloud platforms and data warehouses (Snowflake, Synapse, BigQuery, Redshift, Delta Lake, ADLS, etc.).
• Existing orchestration, transformation, and monitoring tools.
• Current architecture diagrams and technical documentation.
• Development, testing, and production environments.
• Data governance policies and standards.
• Existing cataloging and lineage solutions.
• Security, privacy, and access-control requirements.
• Regulatory and compliance considerations.
• Defined SLAs and operational expectations.
• Existing quality rules and validation frameworks.
• Monitoring and alerting requirements.
• Incident management and escalation processes.
• Agile methodology and delivery processes.
• Backlog priorities and roadmap visibility.
• Team roles, responsibilities, and dependencies.
• Jira boards, workflows, and planning mechanisms.
• Access to key stakeholders and business representatives.
• Collaboration with Data Engineering, BI, Data Science, and ML teams.
• Defined communication channels and governance forums.
• Clear ownership boundaries and decision-making processes.
• Authority to influence architecture and technical standards.
• Visibility into long-term data strategy and roadmap.
• Opportunities to mentor engineers and shape best practices.
• Ownership of complex analytical solutions and platform improvements.
• Clear technical guidance and architectural standards.
• Access to mentoring and knowledge-sharing opportunities.
• Well-defined project scope and priorities.
• Opportunities to progressively increase ownership and technical responsibility.
• English proficiency at a professional level (minimum B2).
• Living in Portugal and available to work in a hybrid model in Lisbon.

At DataSmart, you will have the opportunity to integrate into a solid company and participate in projects of high recognition, nationally and internationally, with an appealing technological environment and career progression.

Excellence in the way to the future!

Connect with Us: Follow us on LinkedIn for updates on career opportunities, company news, and industry insights! For further details, visit us at www.datasmart.pt

#JoinOurTeam #DataSmart #ThinkDataBeSmart #DataModeling #Architecture