{"id":3062254,"url":"https://alion.io/job/unisongroup-senior-data-engineer","title":"Senior Data Engineer","company":{"id":448684,"name":"Unison Group","domain":"unisongroup.com","url":"https://alion.io/company/unisongroup","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":72,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","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":{"min_usd":15500,"max_usd":41000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"MLFlow","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"Splunk","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-14T00:00:00Z","employer_posted_date":"2026-10-14","last_verified_at":"2026-10-26T12:45:28Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"Role Overview\nWe are seeking a hands-on Databricks engineer with deep technical expertise in building, operating, and optimizing Lakehouse-based data and AI solutions. In this role, you will design, develop, and operationalize Delta Lakehouse architectures that deliver measurable outcomes for enterprise customers. You will own implementation activities, contribute to technical delivery leadership, and mentor engineering teams in best practices across data engineering, platform governance, and AI.\nKey Responsibilities\nDesign, build, and maintain scalable data pipelines using Delta Live Tables (DLT) / Lakeflow Spark Declarative Pipelines, Spark SQL, Python, or Scala.\nOptimize batch ETL, streaming, and machine-learning workloads for performance, cost efficiency, scalability, and reliability.\nConfigure and administer Databricks workspaces, Unity Catalog, cluster policies, SQL Warehouses, access controls, and secure governed environments.\nAutomate infrastructure provisioning and application deployments using Terraform, Git, and CI/CD pipelines.\nEstablish platform observability, monitoring, alerting, and cost-management practices using tools such as Splunk, Prometheus, or AWS CloudWatch and equivalent cloud-native tools.\nCollaborate with customers and cross-functional teams to deliver AI/LLM solutions using MLflow, Mosaic AI, and relevant Databricks model-serving and evaluation capabilities.\nTroubleshoot production issues, improve operational reliability, and document architecture, engineering standards, and runbooks.\nProvide technical guidance, conduct code reviews, and mentor engineers on Databricks, data engineering, governance, and DevOps best practices.\nRequired Skills & Experience\nStrong hands-on experience with Databricks, including workspace configuration, notebooks, compute, jobs, and orchestration.\nPractical expertise in Delta Lake, DLT / Lakeflow Spark Declarative Pipelines, Unity Catalog, and Databricks SQL Warehouses.\nProficiency in Python or Scala, with strong SQL skills for data engineering and/or machine-learning workflows.\nGood understanding of at least one major cloud ecosystem: AWS, Microsoft Azure, or Google Cloud Platform (GCP).\nExperience with Terraform or similar infrastructure-as-code tools, Git-based workflows, CI/CD, and DevOps/MLOps 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