Senior data engineer

Required Qualifications

Education:

• Bachelor's degree in Computer Science, Information Technology, Engineering, or related discipline;
• Master's degree preferred.

Experience

• 8+ years of experience in data engineering, data warehousing, or data platform development;
• 5+ years of hands-on experience with Databricks and Apache Spark;
• Experience designing and implementing enterprise-scale cloud data platforms;
• Experience leading technical initiatives and mentoring engineering teams.

Technical Skills

Data Engineering:

• Databricks;
• Apache Spark (PySpark, Spark SQL);
• Delta Lake;
• Delta Live Tables;
• Unity Catalog;
• Data Warehousing and Data Modeling.

Programming:

• Python;
• SQL;
• Scala (preferred);
• PowerShell or scripting automation experience.

Cloud Platforms:

• Microsoft Azure;
• AWS;
• Cloud-native storage and compute services.

Databases:

• SQL Server;
• Oracle;
• PostgreSQL;
• Snowflake (preferred).

Data Integration:

• Azure Data Factory;
• Databricks Workflows;
• Kafka/Event Streaming;
• REST APIs.

DevOps & Automation:

• GitHub;
• Azure DevOps;
• CI/CD pipelines;
• Terraform;
• Infrastructure as Code.

Observability:

• Monitoring and alerting solutions;
• Data quality frameworks;
• Performance optimization;
• Logging and troubleshooting.

AI & Emerging Technologies:

• Machine Learning data pipelines;
• Feature Stores;
• MLOps;
• Generative AI concepts;
• Vector databases;
• Retrieval-Augmented Generation (RAG);
• AI governance and responsible AI principles.

Preferred Qualifications

• Databricks Certified Data Engineer Professional;
• Azure Data Engineer Associate certification;
• AWS Data Analytics certification;
• Experience supporting regulated industries such as Insurance, Financial Services, or Healthcare;
• Experience implementing enterprise data governance programs;
• Experience integrating AI solutions into production environments.

Key Competencies

• Strategic thinking and problem solving;
• Technical leadership;
• Architecture design;
• Stakeholder management;
• Communication and presentation skills;
• Continuous improvement mindset;
• lnnovation and AI adoption;
• Risk and compliance awareness.

Success Measures

• Delivery of scalable, resilient, and secure data solutions;
• Adoption of enterprise engineering standards and best practices;
• Improvement in platform performance, reliability, and cost efficiency;
• Accelerated delivery of analytics and AI capabilities;
• Reduction in operational support effort through automation and observability;
• Successful mentoring and development of engineering talent.

We are seeking a highly skilled Senior Data Engineer to design, build, and optimize modern data platforms that enable advanced analytics, AI/ML, and business intelligence solutions. The ideal candidate will possess deep expertise in Databricks, cloud-based data engineering, data architecture, and modern software engineering practices. 

This role will provide technical leadership, establish engineering standards, and drive the development of scalable, secure, and reliable data solutions supporting enterprise-wide initiatives.

Key Responsibilities

Data Platform Engineering:

• Design, develop, and maintain scalable data pipelines using Databricks, Spark, and cloud-native technologies;
• Build and optimize batch and real-time data ingestion frameworks;
• Develop reusable engineering patterns, frameworks, and accelerators for enterprise data solutions;
• Implement data quality, observability, lineage, and governance capabilities across the data ecosystem;
• Support large-scale data modernization and cloud migration initiatives.

Databricks Engineering:

• Design and implement Delta Lake architectures and Medallion data models;
• Build optimized ETL/ELT pipelines using Databricks notebooks, workflows, and Delta Live Tables;
• Establish performance tuning, cost optimization, and workload management best practices;
• Leverage Unity Catalog for data governance