Data Engineer

• Design, build, and maintain scalable and efficient data pipelines to support analytics, reporting, and operational use cases


• Collaborate closely with product owners, analysts, and data consumers to translate business requirements into reliable data solutions


• Develop and maintain data integration workflows across both cloud-native and on-premises systems


• Champion best practices in data architecture, modelling, and quality assurance to ensure accuracy and performance


• Participate in sprint planning, daily stand-ups, and retrospectives as an active member of a cross-functional agile team


• Identify and remediate technical debt across legacy pipelines and contribute to the modernization of the data platform


• Implement robust monitoring and alerting for pipeline health, data quality, and SLA adherence


• Write and maintain documentation for data flows, transformations, and system dependencies


• Contribute to code reviews and peer development to foster a collaborative and high-quality engineering culture


• Ensure adherence to security, privacy, and compliance standards in all data engineering practices

Requirements

• 5+ years of professional experience in data engineering, analytics engineering, or related fields


• Bachelors degree in computer science, or equivalent field and 2+ years of experience


• Advanced SQL skills, including performance tuning and query optimization


• Expertise in Snowflake, including data warehousing concepts, architecture, and best practices


• Experience with modern data transformation tools (e. g. , dbt)


• Experience building and maintaining automated ETL/ELT pipelines, with a focus on performance, scalability, and reliability


• Proficiency with version control systems (e. g. , Git), working within CI/CD pipelines and experience with environments that depend on infrastructure-as-code


• Experience writing unit and integration tests for data pipelines


• Familiarity with data modeling techniques (e. g. , dimensional modeling, star/snowflake schemas)


• Experience with legacy, on-premise databases such as Microsoft SQL Server is preferred


• Exposure to cloud platforms (e. g. , AWS, Azure, GCP), cloud-native data tools, and data federation tools is a plus


• Experience with Sql Server Reporting Services (SSRS) is beneficial