Lead Data Engineer

Key Responsibilities

• Data Ingestion Framework:

o Design & Development: Architect, develop, and maintain an end-to-end data ingestion framework that efficiently extracts, transforms, and loads data from diverse sources.

o Framework Optimization: Use AWS services such as AWS Glue, Lambda, EMR, ECS , EC2 and Step Functions to build highly scalable, resilient, and automated data pipelines.

• Data Quality & Validation:

o Validation Processes: Develop and implement automated data quality checks, validation routines, and error-handling mechanisms to ensure the accuracy and integrity of incoming data.

o Monitoring & Reporting: Establish comprehensive monitoring, logging, and alerting systems to proactively identify and resolve data quality issues.

• API Development:

o Design & Implementation: Architect and develop secure, high-performance APIs to enable seamless integration of data services with external applications and internal systems.

o Documentation & Best Practices: Create thorough API documentation and establish standards for API security, versioning, and performance optimization.

• Collaboration & Agile Practices:

o Cross-Functional Communication: Work closely with business stakeholders, data scientists, and operations teams to understand requirements and translate them into technical solutions.

o Agile Development: Participate in sprint planning, code reviews, and agile ceremonies, while contributing to continuous improvement initiatives and CI/CD pipeline development (using tools like GitLab).

Required Qualifications

• Experience & Technical Skills:

o Professional Background: At least 5 years of relevant experience in data engineering with a strong emphasis on analytical platform development.

o Programming Skills: Proficiency in Python and/or PySpark, SQL for developing ETL processes and handling large-scale data manipulation.

o AWS Expertise: Extensive experience using AWS services including AWS Glue, Lambda, Step Functions, and S3 to build and manage data ingestion frameworks.

o Data Platforms: Familiarity with big data systems (e.g., AWS EMR, Apache Spark, Apache Iceberg) and databases like DynamoDB, Aurora, Postgres, or Redshift.

o API Development: Proven experience in designing and implementing RESTful APIs and integrating them with external and internal systems.

o CI/CD & Agile: Hands-on experience with CI/CD pipelines (preferably with GitLab) and Agile development methodologies.

• Soft Skills:

o Strong problem-solving abilities and attention to detail.

o Excellent communication and interpersonal skills with the ability to work independently and collaboratively.

o Capacity to quickly learn and adapt to new technologies and evolving business requirements.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field.

• Experience with additional AWS services such as Kinesis, Firehose, and SQS.

• Familiarity with data lakehouse architectures and modern data quality frameworks.

• Prior experience in a role that required proactive data quality management and API- driven integrations in complex, multi-cluster environments.