Senior Data Engineer

The Senior Data Engineer will play a key role in designing, building, and maintaining scalable data pipelines, integrations, and analytical platforms that support CTS's global analytics and reporting platforms. This role is responsible for developing high-quality ETL/ELT processes, managing cloud data environments, ensuring data quality, and enabling advanced analytics and reporting across major programs.

You will work extensively with Azure Data Factory, Azure Databricks, Python, SQL (Oracle/SQL Server), data modelling, data lakes, and enterprise data warehouse structures to support KPI engines, device analytics, predictive modelling, and operational performance insights.

Key Responsibilities:

• Design, build, and optimize data pipelines using Azure Data Factory and Databricks (PySpark/Spark SQL).
• Develop robust ETL/ELT processes to ingest, transform, and validate large volumes of operational, telemetry, incident, and transactional data.
• Implement scalable workflows leveraging Azure services such as Data Lake Storage, Azure Synapse, Azure SQL Databases, Key Vault, Logic Apps, and Functions.
• Build efficient advanced SQL queries and stored procedures across Azure Synapse and Azure SQL Server.
• Collaborate with data analysts, engineers, performance assurance, and operations teams to enable reliable, accurate datasets for reporting, KPIs, and predictive analytics.
• Design and maintain data models, schemas, tables, and metadata following COE architecture patterns.
• Implement strong data quality, validation, and monitoring frameworks to ensure accuracy and reliability across global programs.

Required Skills and Qualifications:

• Bachelor's/master's degree in Computer Science, Data Analytics, Engineering, Mathematics, or related field.
• 5+ years of experience in data engineering roles.
• Strong expertise with Azure Data Factory (ADF) pipelines, Azure Databricks, mapping data flows, and orchestrations.
• Advanced experience with Azure Databricks (PySpark, Spark SQL, Delta Lake, notebooks, clusters).
• High proficiency in Python for ETL, automation, and data transformation.
• Strong SQL skills across Azure Synapse and Azure SQL, including query tuning and complex transformations.
• Solid understanding of data modelling, warehousing, star/snowflake schema, Medallion architecture, and distributed data processing.
• Experience handling large-scale datasets and complex data domains.
• Familiarity with CI/CD practices, Git branching, and DevOps pipelines.
• Experience with data governance, data cataloging, and managing structured/unstructured data in cloud environments.

Preferred Skills:

• Experience with Azure Data Factory (ADF) pipelines, Azure Databricks, Databricks Workflows, or Apache Airflow.
• Exposure to real-time/streaming data technologies (Event Hub, Kafka, Spark Streaming).
• Exposure to machine learning algorithms, feature engineering, model evaluation, hyperparameter tuning, LLM fundamentals, and scikit-learn/XGBoost. Knowledge of deep learning, PyTorch/TensorFlow, MLflow, model deployment, and MLOps practices.
• Experience integrating APIs, flat files, and operational systems such as ServiceNow.
• Experience with machine learning pipelines, feature stores, or model operationalization.
• Familiarity with Power BI dataset design and optimization.
• Understanding of multi-region cloud environments and enterprise architecture patterns.

Soft Skills:

• Strong analytical mindset with exceptional problem-solving abilities.
• Ability to work independently and take ownership of deliverables.
• Excellent communication skills, capable of simplifying complex concepts for non-technical stakeholders.
• Comfortable working in a fast-paced, global, multi-time zone team.
• High attention to detail, data quality, and accuracy.
• Strong sense of accountability, adaptability, and continuous improvement.
• Ability to engage effectively with engineering, operations, and business stakeholders.
• Proactive, resourceful, and committed to delivering high-quality outcomes.

Role Impact & Success Measures:

The Senior Data Engineer will design, build, and maintain enterprise data warehouse solutions for analytics and reporting, develop and optimize ETL/ELT pipelines to load and transform data from multiple sources. Implement dimensional modelling (star/snowflake schemas) for performance and scalability, and ensure data quality, governance, and performance tuning across the warehouse whilst strengthening solutions using Lakehouse, Data Warehouse, and Real-Time Analytics.

Success in the First 3-6 Months:

• Build a solid understanding of CTS datasets, KPI frameworks, and key operational systems.
• Gain strong understanding of CTS datasets, pipelines, and Azure architecture.
• Deliver reliable ADF and Databricks pipelines for ingestion and transformation.
• Improve data quality and performance of existing workflows.
• Support analysts and operations teams with accurate, well-structured datasets.

Success in 6-12 Months:

• Take ownership of end-to-end analytical workstreams with minimal supervision.
• Own end-to-end pipeline development with minimal supervision.
• Implement scalable, reusable engineering patterns and metadata-driven frameworks.
• Improve efficiency through automation, orchestration, and optimization.
• Integrate new data sources to support KPIs, device analytics, and predictive modelling.

Long-Term Success (12+ Months):

• Contribute to CTS Analytics strategy, standards, and global operating model improvements.
• Contribute to global COE standards for data engineering and cloud architecture.
• Become SME for key pipelines, data domains, or Azure components.
• Lead continuous improvement initiatives around quality, governance, and automation.
• Support junior engineers and uplift engineering capability within the COE.

Skills

ADF, Azure Data Factory, Azure SQL, Azure Synapse, Databricks, Delta Lake, PySpark, Python, SQL, Spark SQL