Job Description
InQube is a global apparel innovation company with capabilities in Design, Technology Incubation and execution at scale. Our presence in the UK, US, Hong Kong and China enables consumer insight and rapid prototyping, while Sri Lanka and Cambodia execute at scale with precision. We built a proprietary tech stack to solve pain points – delivering comfort, fit & functionality across intimates, athleisure, performance and casual segments.
SENIOR EXECUTIVE /ASSISTANT MANAGER – DATA ENGINEER
BASED AT ATHURUGIRIYA
Key Responsibilities
- Design, develop, and maintain robust, scalable ETL/ELT pipelines to ingest data from multiple internal and external sources
- Build and optimize data warehouses, data lakes, and data marts to support business intelligence and analytics needs
- Ensure data quality, integrity, and consistency through validation, monitoring, and testing frameworks
- Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and deliver fit-for-purpose solutions
- Optimize database performance, query efficiency, and storage costs across cloud and/or on-premise environments
- Implement and maintain data governance, security, and compliance standards (e.g., access controls, data privacy policies)
- Automate manual data processes to improve efficiency and reduce errors
- Monitor data pipelines in production, troubleshoot issues, and implement fixes proactively
- Document data flows, architecture, and technical processes for knowledge sharing
- Evaluate and recommend new tools, technologies, and frameworks to improve the data ecosystem
- Engage in data analytics activities
- Support ad-hoc data requests and reporting needs from cross-functional teams
Qualifications
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (Master's degree is a plus)
- Minimum 3-6 years of experience in data engineering, database development, or a related field
- Proven experience designing and maintaining production-grade ETL/ELT pipelines
- Experience working with large-scale datasets and distributed data systems
- Strong proficiency in SQL and at least one programming language (Python, Java, or Scala)
- Hands-on experience with data pipeline/orchestration tools (e.g., Apache Airflow, dbt, Luigi)
- Experience with cloud data platforms (AWS, Azure, or GCP — e.g., Redshift, BigQuery, Snowflake, Databricks)
- Hands-on experience with Microsoft Fabric (e.g., Lakehouse, Data Factory pipelines, Dataflows Gen2, OneLake) is an advantage
- Familiarity with big data technologies (e.g., Spark, Hadoop, Kafka) is an advantage