The Role
Design, build, and maintain scalable cloud data platforms and ETL/ELT pipelines using AWS (Glue, Lambda, Redshift, S3) and Python/PySpark. Implement data warehousing, modeling, governance, monitoring, and performance optimization. Collaborate with stakeholders, mentor junior engineers, support production deployments, and drive cloud modernization and best practices to enable analytics and ML initiatives.
Summary Generated by Built In
Lead Data Engineer
Primary Skills
- ETL Fundamentals, SQL, BigQuery, Dataproc, SQL (Basic + Advanced), Python, Data Catalog, Data Warehousing, Composer, Dataflow, Cloud Trace, Cloud Logging, Cloud Storage, Datafusion, Modern Data Platform Fundamentals, Data Modelling Fundamentals, PLSQL, T-SQL, Stored Procedures
Specialization
- AWS Data Engineering Basic: Senior Data Engineer
Job requirements
- We are seeking a highly skilled and motivated Senior Data Engineer with 8–10 years of experience in designing, building, and maintaining scalable cloud-based data platforms. The ideal candidate will have strong expertise in AWS data services, modern data engineering practices, and data warehousing solutions. This role requires a hands-on engineer who can collaborate with cross-functional teams, translate business requirements into technical solutions, and drive the development of robust, high-performance data pipelines that support analytics, reporting, and AI/ML initiatives. Key Responsibilities Design, develop, and maintain scalable and reliable data pipelines, ETL/ELT frameworks, and data integration solutions. Build cloud-native data solutions using AWS services including Glue, Lambda, Redshift, Aurora, OpenSearch, Step Functions, SNS, and S3. Develop and optimize data processing workflows using Python, PySpark, SQL, and PL/SQL. Design and implement data warehouse solutions, data marts, and data models to support enterprise reporting and analytics. Work closely with business stakeholders, product teams, data scientists, and analysts to understand requirements and deliver high-quality data solutions. Ensure data quality, integrity, governance, security, and compliance across the data ecosystem. Optimize database performance, query execution, and large-scale data processing workloads. Implement monitoring, alerting, and troubleshooting mechanisms to ensure platform reliability and operational excellence. Participate in solution design discussions, architecture reviews, and cloud modernization initiatives. Mentor junior team members and promote engineering best practices, code quality standards, and knowledge sharing. Support production deployments, issue resolution, and continuous improvement activities. Required Skills & Qualifications Experience · 8–10 years of experience in Data Engineering, Data Warehousing, and Cloud Data Platform development. · Proven experience delivering enterprise-scale data engineering solutions in cloud environments. · AWS Cloud Technologies Strong hands-on experience with: Cloud Technologies: AWS Glue, Lambda, Redshift, Aurora, OpenSearch, Step Functions, SNS, S3 Programming & Data Engineering Skills: Python, PySpark, SQL, PL/SQL, Data Warehousing Programming & Data Engineering · Advanced proficiency in Python and PySpark for large-scale data processing. · Strong expertise in SQL and PL/SQL development. · Experience building and optimizing ETL/ELT pipelines and data ingestion frameworks. · Solid understanding of data warehousing concepts, dimensional modeling, and database design. · Experience working with structured, semi-structured, and unstructured data. · Data Architecture & Platform Engineering · Strong understanding of modern data architectures, including Data Lakes and Data Warehouses. · Experience designing scalable, secure, and high-performance data solutions. · Knowledge of data quality frameworks, governance practices, and data lifecycle management. · Experience with workflow orchestration, event-driven architectures, and distributed data processing. Professional Skills · Strong analytical and problem-solving capabilities. · Excellent communication and stakeholder management skills. · Ability to work independently and collaboratively in a fast-paced environment. · Strong ownership mindset with a focus on quality, reliability, and continuous improvement. Preferred Qualifications · Experience supporting Data Science, Machine Learning, AI, or Advanced Analytics initiatives. · Exposure to modern Data Lakehouse architectures. · Experience with CI/CD pipelines, DevOps practices, and Infrastructure as Code (IaC). · Experience working in Agile/Scrum delivery environments. · AWS Certifications such as AWS Certified Data Engineer, Solutions Architect, or equivalent cloud certifications. · Success Criteria The successful candidate will be able to: · Deliver scalable and reliable cloud-base
Skills Required
- 8-10 years of experience in Data Engineering, Data Warehousing, and Cloud Data Platform development
- Proven experience delivering enterprise-scale data engineering solutions in cloud environments
- Strong hands-on experience with AWS Glue, Lambda, Redshift, Aurora, OpenSearch, Step Functions, SNS, and S3
- Advanced proficiency in Python and PySpark for large-scale data processing
- Strong expertise in SQL and PL/SQL development (including T-SQL and stored procedures)
- Experience building and optimizing ETL/ELT pipelines and data ingestion frameworks
- Solid understanding of data warehousing concepts, dimensional modeling, and database design
- Experience working with structured, semi-structured, and unstructured data
- Strong understanding of modern data architectures including Data Lakes and Data Warehouses
- Knowledge of data quality frameworks, governance practices, and data lifecycle management
- Experience with workflow orchestration, event-driven architectures, and distributed data processing
- Implement monitoring, alerting, and troubleshooting mechanisms to ensure platform reliability
- Mentor junior team members and promote engineering best practices and code quality standards
- Experience supporting Data Science, Machine Learning, AI, or Advanced Analytics initiatives
- Exposure to modern Data Lakehouse architectures
- Experience with CI/CD pipelines, DevOps practices, and Infrastructure as Code (IaC)
- Experience working in Agile/Scrum delivery environments
- AWS Certifications (e.g., Data Engineer, Solutions Architect) or equivalent
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
