{"id":2837539,"url":"https://alion.io/job/manulife-full-stack-software-engineer-12","title":"Full-Stack Software Engineer","company":{"id":6650,"name":"Manulife","domain":"manulife.com","url":"https://alion.io/company/manulife","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":93,"open_postings":93,"ghost_share":0,"stale_share":0.247,"repost_share":0.043,"time_to_fill_p50_days":27,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Backend","role_family":"Backend","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kuala Lumpur, Malaysia"],"countries":["MY"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"CI/CD","optional":true},{"name":"Databricks","optional":true},{"name":"Machine Learning","optional":true},{"name":"Spark","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-19T07:31:53Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-22T08:59:39Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"We are seeking a Data Engineer with 2-3 years of hands-on experience in building, maintaining, and optimizing data pipelines and data platforms. This role will work closely with analytics, data science, and business teams to ensure high-quality, reliable, and scalable data for reporting, insights, and downstream applications.\nThe ideal candidate has strong SQL skills, experience with cloud-based data platforms, and a solid understanding of modern ELT/ETL practices.\nPosition Responsibilities\n\nDesign, build, and maintain reliable and scalable data pipelines for batch and (where applicable) near real-time data processing\nDevelop and manage ELT/ETL workflows to ingest data from multiple sources (databases, APIs, files)\nBuild and optimize data models to support analytics, BI, and reporting use cases\nEnsure data quality, accuracy, and consistency through validation, monitoring, and testing\nCollaborate with analysts, data scientists, and product teams to understand data requirements\nTroubleshoot and resolve data pipeline failures and performance issues\nMaintain documentation for data pipelines, data models, and operational processes\nFollow best practices in security, access control, and cost optimization for data platforms\nRequired Qualifications\n\nBachelor’s Degree in Computer Science, Engineering, Data Science, or a related field\n2-3 years of professional experience in Data Engineering or related roles\nStrong proficiency in SQL (complex queries, joins, window functions)\nExperience with Python (or similar language) for data processing and automation\nHands-on experience with cloud data platforms (AWS, Azure, or GCP)\nFamiliarity with data warehouses (e.g., Snowflake, BigQuery, Redshift, Synapse)\nExperience with ETL/ELT tools or frameworks (dbt, Airflow, Fivetran, Azure Data Factory, etc.)\nUnderstanding of data modeling concepts (dimensional modeling, fact & dimension tables)\nExperience working with version control (Git)\nGood problem-solving, communication, and collaboration skills\nPreferred Qualifications\n\nExperience with distributed processing frameworks (Spark / Databricks)\nExposure to streaming data (Kafka, Event Hubs, Kinesis)\nKnowledge of data quality or observability tools\nBasic understanding of DevOps/IaC (Terraform, CI/CD pipelines)\nExperience supporting machine learning or GenAI data pipelines\nWhen You Join Our Team\n\nWe’ll empower you to learn and grow the career you want.\nWe’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.\nAs part of our global team, we’ll support you in shaping the future you want to see. \nAbout Manulife and John Hancock\nManulife Financial Corporation is a leading international financial services provider, helping people make their decisions easier and lives better. To learn more about us, visit https://www.manulife.com/en/about/our-story.html.\nManulife is an Equal Opportunity Employer\nAt Manulife/John Hancock, we embrace our diversity. We strive to attract, develop and retain a workforce that is as diverse as the customers we serve and to foster an inclusive work environment that embraces the strength of cultures and individuals. We are committed to fair recruitment, retention, advancement and compensation, and we administer all of our practices and programs without discrimination on the basis of race, ancestry, place of origin, colour, ethnic origin, citizenship, religion or religious beliefs, creed, sex (including pregnancy and pregnancy-related conditions), sexual orientation, genetic characteristics, veteran status, gender identity, gender expression, age, marital status, family status, disability, or any other ground protected by applicable law.\nIt is our priority to remove barriers to provide equal access to employment. A Human Resources representative will work with applicants who request a reasonable accommodation during the application process. All information shared during the accommodation request process will be stored and used in a manner that is consistent with applicable laws and Manulife/John Hancock policies. 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