Senior Data Engineer at NorthBay Solutions

, Punjab, Pakistan -

Full Time

Start Date

Immediate

Expiry Date

25 Sep, 26

Salary

0.0

Posted On

27 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

PySpark, AWS Glue, Amazon EMR, Amazon Redshift, SQL, Data Warehousing, ETL/ELT, Performance Tuning, Dimensional Modeling, Distributed Data Processing, AWS S3, AWS Lambda, CI/CD, DevOps, Workflow Orchestration, Data Governance

Industry

IT Services and IT Consulting

Description

Position: Senior Data Engineer Experience: 5–8 Years Location: Hybrid (Pakistan) Note: (Candidates must be available during UAE business hours and follow UAE public holidays) Job Summary We are seeking a highly skilled Senior Data Engineer with 5–8 years of experience in designing, developing, and optimizing large-scale data platforms and ETL/ELT pipelines. The ideal candidate will have strong hands-on expertise in PySpark, AWS Glue, Amazon EMR, Amazon Redshift, and SQL-based data warehousing, along with proven experience in performance tuning and data optimization. The candidate will work closely with a UAE-based customer and must be comfortable collaborating with distributed teams while adhering to UAE working hours and holiday schedules. Key Responsibilities Design, develop, and maintain scalable data pipelines using PySpark, AWS Glue, and Amazon EMR. Build and optimize data ingestion, transformation, and processing frameworks for structured and semi-structured data. Develop and maintain enterprise data warehouse solutions using Amazon Redshift. Write complex SQL queries, stored procedures, and data transformations to support analytics and reporting requirements. Implement ETL/ELT processes to move data efficiently across multiple systems and platforms. Perform performance tuning and optimization of Spark jobs, ETL pipelines, SQL queries, and Redshift workloads. Ensure data quality, integrity, security, and governance across data platforms. Troubleshoot production issues and perform root cause analysis for data-related incidents. Collaborate with business stakeholders, analysts, architects, and engineering teams to understand data requirements. Participate in code reviews, technical design discussions, and best-practice implementation. Monitor data pipelines and proactively identify opportunities for performance improvements and automation. Create and maintain technical documentation, data models, and operational procedures. Required Skills & Experience Must-Have Skills 5–8 years of experience in Data Engineering and Data Warehousing. Strong hands-on experience with PySpark. Extensive experience with AWS Glue. Experience building and managing workloads on Amazon EMR. Strong expertise in Amazon Redshift. Excellent SQL development and query optimization skills. Strong understanding of Data Warehousing concepts, dimensional modeling, and ETL/ELT processes. Experience in performance tuning of Spark jobs, SQL queries, ETL pipelines, and data warehouse workloads. Experience handling large-scale datasets and distributed data processing. Strong debugging, troubleshooting, and analytical skills. Preferred Skills Experience with additional AWS services such as S3, IAM, CloudWatch, Lambda, and Step Functions. Knowledge of CI/CD pipelines and DevOps practices for data platforms. Experience with workflow orchestration tools. Familiarity with data governance, security, and compliance practices. Exposure to Agile/Scrum development methodologies. Qualifications Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field. Relevant AWS certifications will be considered an advantage. Soft Skills Strong communication and stakeholder management skills. Ability to work independently in a remote environment. Excellent problem-solving and analytical thinking abilities. Ability to collaborate effectively with cross-functional and geographically distributed teams. Strong ownership mindset and commitment to delivering high-quality solutions.

Responsibilities

Design, develop, and optimize scalable data pipelines and enterprise data warehouse solutions using PySpark and AWS services. Ensure data quality and performance tuning for large-scale datasets to support analytics and reporting.