{"id":596521,"url":"https://alion.io/job/simplifynext-data-engineer","title":"Data Engineer","company":{"id":456201,"name":"SimplifyNext","domain":"simplifynext.com","url":"https://alion.io/company/simplifynext","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"D","score":51,"open_postings":28,"ghost_share":0.821,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":45000,"max_usd":90000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":30},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Java","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"ServiceNow","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"UiPath","optional":false},{"name":"Azure","optional":true},{"name":"Looker","optional":true},{"name":"Power BI","optional":true},{"name":"RAG","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-09-10T01:01:05Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-29T07:38:22Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":49},"description":"SimplifyNext is a Singapore-headquartered digital engineering firm, with offices in Thailand and Malaysia, working across three converged areas: agentic AI, AI-enabled application modernisation, and intelligent automation.\nWe bring strong business process consulting together with deep technology skills and modern delivery practices - product-centric, agile and AI-assisted. We build bespoke applications and AI systems, and we go deep on the platforms our clients run on, including Microsoft, AWS, ServiceNow, Databricks and UiPath. That combination of engineering depth and platform depth is what our clients come to us for.\nWe build and modernise the systems people depend on daily. Our 300+ practitioners are multi-disciplinary by design - business consultants, software engineers, architects, AI engineers and designers working on the same delivery, not handing off between silos. We focus on delivering outcomes for clients, building strong careers for our people, and staying ahead of the technology curve. We hire because we are growing.\nRole Purpose\nThe Data Engineer designs, builds and deploys scalable data lakehouse platforms for our clients - architecting end-to-end pipelines, establishing data quality frameworks, and delivering production-ready solutions that form the foundation of an organisation's data infrastructure. You will work across modern lakehouse stacks (Databricks, Spark, Microsoft Fabric or AWS) alongside architects, AI engineers and business analysts on the same delivery, not in a separate data silo.\nWhat You'll Own\nData Pipeline & Architecture Design\nDesign and implement scalable ETL/ELT pipelines on modern cloud data platforms - AWS (Glue, Step Functions, Lambda, S3), Databricks, Spark or Microsoft Fabric.\nArchitect and build data lakehouse solutions using open table formats such as Apache Iceberg, Delta Lake or S3 Tables, including schema evolution, partition evolution and ACID transactions.\nOptimise pipelines for performance, cost and reliability at enterprise scale.\nData Quality & Governance\nDefine, implement and maintain automated data quality validation frameworks, with metrics and monitoring for accuracy, completeness and consistency.\nEnforce data governance standards, access controls, lineage and PII handling across the platform, appropriate to public sector and regulated client environments.\nApplication Development & Deployment\nWrite production-quality code and deploy solutions on cloud infrastructure, including Government Commercial Cloud (GCC) environments.\nApply DevOps practices - CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation or equivalent) - for repeatable and auditable deployments.\nAI & Analytics Enablement\nPrepare and serve data for AI and agentic workloads - feature pipelines, retrieval sources and knowledge bases.\nSupport analytics and BI teams with reliable, well-modelled datasets and clear semantics.\nCollaboration, Handover & Day 2 Operations\nWork across cross-functional teams and communicate technical designs clearly to non-technical stakeholders.\nProduce thorough technical documentation to support handover to Day 2 operations teams.\nOwn the pipelines you build in production - monitoring, incident response, root-cause fixes and cost tuning as data volumes grow.\nAI-Assisted Delivery\nWe deliver with an in-house AI workbench that pairs practitioners and AI across the lifecycle. As a Data Engineer you'll use it to:\nAccelerate pipeline scaffolding, transformation logic and test data generation.\nSupport schema mapping, profiling and technical documentation of source systems.\nAssist with code review, data quality rule suggestion and runbook drafting.\nAI accelerates the work; you own the design decisions, the data correctness and the outcome.\nWhat You'll Bring\nMust-Have\nDegree in Computer Science, Data Engineering, Information Systems or a related field.\n4+ years of hands-on experience in data engineering, ETL/ELT development or data platform roles, building and operating production pipelines.\nStrong hands-on experience with at least one modern data platform - Databricks, Apache Spark, Microsoft Fabric or AWS (Glue, Step Functions, Lambda, S3, Redshift).\nProficiency with open table formats and lakehouse architecture - Apache Iceberg, Delta Lake or S3 Tables - including schema evolution, partitioning and ACID transactions.\nStrong SQL and proficiency in Python (or Scala/Java) for data processing and automation.\nExperience designing data models and warehouse/lakehouse layers for analytics, reporting and AI workloads.\nExperience defining and automating data quality validation, monitoring and reconciliation.\nWorking knowledge of DevOps for data - CI/CD, version control and infrastructure-as-code (Terraform, CloudFormation or equivalent).\nAbility to work directly with business and technical stakeholders and communicate technical designs clearly to non-technical audiences.\nGood-to-Have\nExperience delivering data projects in the Singapore Public Sector, particularly in a Government Commercial Cloud (GCC / GCC+) environment.\nCloud or platform certification - AWS Certified Data Analytics, AWS Certified Solutions Architect, Databricks Data Engineer, or Azure/Fabric Data Engineer.\nExposure to AI/ML workloads - feature pipelines, vector stores, RAG data preparation or MLOps.\nExperience with data migration from legacy systems, including reconciliation and cutover.\nFamiliarity with governance frameworks and PII handling (e.g. masking, tokenisation, Presidio).\nExperience with BI and semantic layers (Power BI, Tableau, Looker) and enabling self-service analytics.\nNot for You If\nThis role is not a fit if:\nYou want to work only on ad-hoc analysis and dashboards rather than building and running pipelines.\nYou see data quality, testing and documentation as someone else's job.\nYou're uncomfortable supporting what you build once it's in production.\nYou prefer a single fixed tech stack over learning new platforms as client needs change.\nYou treat technical documentation and operational handover as an afterthought.\nWhy SimplifyNext\n\nOne Team, Not Siloed Offerings\n\nApps, automation, and AI aren't separate teams here - you'll work across all three as one connected practice, not get boxed into a single lane.\n\nAI in Our DNA, Not Bolted On\n\nRegardless of platform, every specialist here is AI-enabled by design, powered by our own in-house AI workbench that pairs humans and AI from discovery to testing.\n\nFuture-Proof Your Career\n\nTech trends come and go - today's hot platform is tomorrow's afterthought. We multi-skill our people across platforms and AI so your career keeps growing no matter where the market moves.\n\nAPAC's Biggest AI-Integrated Practice\n\nAcross every platform we work in, our AI-integrated practice is the largest in the region, with work good enough to be cited at Google's own AI event.\n\nGrow With Us Across ASEAN\n\nWe're one of Singapore's 100 Fastest Growing Companies, expanding fast across Thailand and Malaysia - plenty of room to grow with us.\n\nAt SimplifyNext, we’re committed to building a team of curious, driven, and forward-thinking individuals who care deeply about creating meaningful impact through technology. 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