SL Transformation-Senior Data Engineer
Summary
Designs and maintains scalable data pipelines, warehouses, and lakehouse architectures to support analytics and business transformation, using SQL, cloud platforms, and distributed processing tools like Spark.
Join a leading independent technology and services provider as a Senior Data Engineer!Job Overview:The Senior Data Engineer is responsible for designing, developing, and maintaining scalable data platforms and pipelines that support analytics, reporting, and business transformation initiatives. The role requires expertise in data engineering, cloud technologies, data modeling, performance optimization, and collaboration with cross-functional teams to deliver reliable and high-quality data solutions.Location: London Rate: An hourly rate, in-scope IR35 £443 per Day (via a Hays Approved Umbrellapany).Contract Length: 23/10/2026Work Schedule: 2 - 3 days onsite/weekKnowledge & Experience
- Design, develop, and maintain robust ETL/ELT pipelines for structured and unstructured data.
- Build scalable data ingestion, transformation, and storage solutions using modern data engineering frameworks.
- Develop and optimize data models, data warehouses, and lakehouse architectures.
- Ensure data quality, integrity, security, andernance across data platforms.
- Collaborate with business stakeholders, data analysts, architects, and product teams to understand requirements and deliver solutions.
- Monitor and troubleshoot data pipelines, resolving performance bottlenecks and operational issues.
- Implement CI/CD, automation, and DevOps practices for data engineering workflows.
- Work with cloud-native services and distributed data processing technologies.
- Support data migration and modernization initiatives as part of transformation programs.
- Mentor junior engineers and contribute to technical best practices and standards.
- Strong experience in SQL and data modeling concepts.
- Hands-on experience with ETL/ELT development.
- Proficiency in Python, Scala, or Spark-based data processing.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Knowledge of Data Warehouse and Data Lake architectures.
- Experience with orchestration tools such as Airflow, ADF, or equivalent.
- Understanding of dataernance, data quality, and security practices.
- Familiarity with CI/CD pipelines and version control tools.
- Experience with Databricks, Snowflake, Redshift, Synapse, or BigQuery.
- Knowledge of real-time streaming technologies such as Kafka.
- Exposure to Agile delivery methodologies.
- Experience in large-scale data transformation or modernization programs.
- Expectedpetencies
- Strong analytical and problem-solving skills.
- Excellent stakeholdermunication and collaboration.
- Technical leadership and mentoring capability.
- Ability to work independently and drive delivery in a fast-paced environment.