Senior Analytics Engineer - Databricks / BI

About Dateonic & the project

Big Data and AI are in our DNA, with modern cloud data platforms and Databricks at the core of our innovation. Founded by engineers and architects passionate about tackling complex data challenges, Dateonic combines industry-leading technical expertise with a product-focused approach to turn raw data into actionable intelligence. Our culture is driven by three core values: Passion, Community, and Commitment. We love tough problems, support each other through close collaboration, and deliver what we promise without taking shortcuts.

In this outsourcing role, you will represent Dateonic on a long-term initiative partnering with a global leader in experience innovation. You will join a multidisciplinary team delivering reliable data pipelines, automated reporting, and executive-ready analytics for world-renowned enterprise brands. Joining this project allows you to drive impact at the intersection of data engineering and business intelligence, supported by the technical mentorship and engineering community of Dateonic.

The role

We are looking for an experienced Senior Analytics Engineer to bridge the gap between backend cloud data platforms and front-end business intelligence. In this role, you will design and maintain cloud-based ETL and ELT workflows, build robust data models, and develop automated reporting solutions using tools such as Databricks, Alteryx, Tableau, and Power BI.

You will alternate between strategic data projects and quick-turn analytical requests, working largely independently to translate complex business questions into scalable, analysis-ready datasets. Beyond technical pipeline building, you will serve as a key partner to data analysts, data scientists, AI specialists, and executive stakeholders to ensure metric consistency, data quality, and governed delivery.

Responsibilities

• Lead the development and maintenance of reusable, analysis-ready datasets and models tailored for reporting, BI dashboards, and advanced analytics.

• Design and implement scalable ingestion and transformation pipelines via APIs and cloud connectors using Alteryx, Databricks SQL, PySpark, and Azure services.

• Build, maintain, and enhance interactive dashboards and executive-level visualisations in Tableau and Power BI, translating ambiguous business questions into actionable insights.

• Orchestrate and automate ETL and ELT workflows utilising tools such as Databricks Workflows, Apache Airflow, or Azure Data Factory.

• Own data modelling, semantic layer consistency, and metric definitions to ensure alignment across analytical assets and business KPIs.

• Perform rigorous data validation, QA, and automated anomaly detection to guarantee data integrity, completeness, and reliability.

• Troubleshoot complex data modelling, transformation, or metric discrepancy issues, conducting root cause analysis to provide lasting resolutions.

• Maintain clear technical documentation, source-to-target dependencies, and semantic definitions, applying version control and code review best practices.

• Collaborate closely with data analysts, data scientists, AI teams, and client stakeholders to align data engineering deliverables with downstream machine learning and reporting needs.

Must-have requirements

• At least 3 years of hands-on experience in analytics engineering, cloud data platforms, and enterprise BI solutions.

• Strong proficiency with the Databricks platform, including Python, PySpark, SQL, Delta Lake, and Databricks Workflows.

• Demonstrated hands-on experience with Alteryx for automated ETL, data preparation, and reporting workflows.

• Extensive experience developing interactive dashboards and visualisations in Tableau, Power BI, or equivalent enterprise BI tools.

• Working knowledge of Microsoft Azure and cloud data architectures.

• Practical experience with API integrations, data ingestion pipelines, and workflow orchestration (such as Databricks Workflows, Airflow, or Azure Data Factory).

• Solid understanding of dimensional data modelling, semantic layers, data validation, and automated quality checks.

• Familiarity with data governance, security standards, and Git-based version control workflows.

• Strong analytical problem-solving abilities and clear English communication skills for collaborating with technical and business stakeholders.

• Degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or a related field (or equivalent practical experience).

Nice to have

• Familiarity with corporate finance KPIs and revenue metrics.

• Experience working with spatial or geospatial datasets.

• Exposure to collaborating with Data Science and AI teams on model feature preparation and AI-driven data assets.

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