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Company Overview
This leading global consulting and technology services organization provides enterprise-scale solutions across cloud, data, and analytics. It partners with clients to deliver innovative, scalable, and secure data engineering solutions, helping organizations leverage advanced technologies to achieve strategic business outcomes.
Job Description Responsibilities
• Lead the development, implementation, and scaling of advanced data engineering solutions using Databricks across AWS, Azure, or GCP environments.
• Establish, document, and promote best practices for data architecture, integration, and modeling.
• Oversee the design, development, and maintenance of robust data pipelines and architectures supporting enterprise-scale data needs.
• Initiate and manage efforts to improve data quality, operational efficiency, and process scalability.
• Evaluate, pilot, and integrate new big data and analytics technologies to maintain technological leadership.
• Consult on, design, and implement governance, security, and compliance strategies tailored to cloud data ecosystems.
• Lead, coach, and develop teams of data engineers and architects to foster technical growth and effective project delivery.
• Communicate technical concepts and business value to diverse stakeholders, including executives and business teams.
• Oversee the implementation of CI/CD practices using tools such as Azure DevOps, AWS Code Pipeline, Jenkins, TFS, or PowerShell for streamlined deployments.
• Manage engagement planning, budgeting, risk, and delivery of work products to ensure quality and timely execution.
Qualifications
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (Master’s preferred).
• 6+ years of hands-on experience in data engineering with strong focus on Databricks on any major cloud (AWS, Azure, GCP).
• Expertise with cloud-native databases, storage solutions, and distributed compute platforms.
• Deep understanding of Lakehouse architecture, Apache Spark, Delta Lake, and related big data technologies.
• Advanced skills in data warehousing, 3NF, dimensional modeling, and enterprise-level data lakes.
• Experience with Databricks components including Delta Live Tables, Autoloader, Structured Streaming, Databricks Workflows, and orchestration tools (e.g., Apache Airflow).
• Hands-on experience with Databricks Unity Catalog and implementing fine-grained security and access control.
• Proven track record in deploying code and solutions via automated CI/CD pipelines.
• Minimum 4 years of leadership experience managing complex, cross-functional data projects and teams.
• Ability to optimize performance of data engineering pipelines, code, and compute resources.
• Ability to travel up to 50% based on client engagements.
• Preferred: Knowledge of AWS, Azure, and GCP ecosystems, performance tuning in Databricks/Apache Spark, Databricks Lakeflow experience, and AI/ML exposure.
• Exceptional communication and stakeholder management skills, including engagement with executive leadership.
Disclaimer
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No Client Relationship: This company is not a client of SpringCube unless stated.
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