At a Glance
- Tasks: Transform complex data into actionable insights and drive operational efficiency.
- Company: Dynamic Life Sciences company focused on innovation and collaboration.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Fast-paced environment with a focus on cutting-edge technologies and teamwork.
- Why this job: Make a real impact by shaping data strategies that guide business decisions.
- Qualifications: 10+ years in Data Engineering and Business Data Analysis with strong SQL skills.
The predicted salary is between 63000 - 77000 £ per year.
In this role, you bridge data engineering and executive decision-making to turn complex data into scalable insights. You design robust data pipelines and apply AI-driven profiling and anomaly detection to support business decisions. You will help drive operational efficiency and revenue growth by delivering actionable BI and analytics in a fast-paced Life Sciences environment. You collaborate with cross-functional teams to ensure reliable data foundations and meaningful reporting. This position offers impact through shaping data architectures and analytics that guide strategic outcomes.
Responsibilities
- Architect and implement AI-powered data profiling, anomaly detection, and automated mapping workflows across enterprise systems.
- Apply predictive analytics to identify process optimization opportunities and enhance customer experience.
- Document profiling, source-to-target mapping, and data transformation rules using AI-assisted methods.
- Gather complex business requirements and translate them into functional designs and scalable specifications.
- Drive cross-functional alignment between business teams, data engineers, and BI developers to ensure reporting accuracy.
- Conduct trend identification, KPI development, and advanced SQL data exploration for operational insights.
- Build, optimize, and oversee scalable SQL and Spark-based data pipelines for high-volume processing.
- Leverage Databricks, Delta Lake, and modern cloud technologies for reliable, incremental, versioned ingestion and transformation.
- Implement reusable data quality checks and proactive troubleshooting across ingestion layers to minimize downtime.
- Support BI dashboard generation and modern reporting infrastructure through structured data model reviews.
Key requirements
- 10-12+ years of experience spanning Data Engineering, ETL Development, and Business Data Analysis.
- 7-10 years of related work experience.
- Proven expertise in SQL, data warehouse architecture, and ETL/Databricks pipeline development.
- Strong experience with Databricks, Delta Lake, and Spark-based pipelines.
- Ability to translate complex business needs into functional technical specifications.
- Experience enabling BI dashboards and reporting infrastructure.
- Stakeholder management.
- Cross-functional collaboration.
- Analytical mindset and strong communication.
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