{"id":1598152,"url":"https://alion.io/job/manila-recruitment-senior-azure-data-engineer","title":"Senior Azure Data Engineer (Hybrid)","company":{"id":1582,"name":"Manila Recruitment","domain":"manilarecruitment.com","url":"https://alion.io/company/manila-recruitment","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":10,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":50,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Muntinlupa, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":15500,"max_usd":40000,"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":"AI Agents","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Power BI","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Azure","optional":true}],"status":"live","first_seen_at":"2026-10-01T18:30:03Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-26T11:52:08Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"Key Accountabilities/BAU Objectives:\nPipelines from Source to Reporting\nDesign, build and maintain the pipelines across the medallion layers, from raw ingestion through to the datasets reporting depends on. You own yours end to end, including how they behave when they fail.\nPipelines delivered end to end without needing a rebuild\nRefresh schedules met, with failures caught by monitoring rather than by a user\nPipelines built to the team standard so someone else can pick them up\nEngineering Depth and Data Design\nTransformations are production code: readable, tested and maintainable. Idempotency and safe replay, correct incremental logic, modelling for how the data is queried, and the judgement to see that generated SQL is the wrong shape even when it returns the right number today.\nTransformation logic readable, tested and reusable rather than one off\nPipelines that replay and backfill correctly by design\nWork another engineer can pick up without a handover conversation\nIngestion from Product Sources\nBring data in from operational systems through gateways or equivalent connectors, handling incremental loads, schema drift and late arriving data without silent loss.\nSource data landed completely and repeatedly, reconciled against the source\nIncremental loads correct on replay and on backfill\nSchema changes detected and handled rather than discovered downstream\nData Quality and Schema Consistency\nPut quality checks in at each layer and keep schemas consistent across them. Where something is wrong, find where it entered rather than patching the layer it surfaced in.\nValidation at each layer boundary, with failures visible and owned\nDefects traced to the layer they entered and fixed there\nA quality check added for every data issue that reached a report\nReporting and the Semantic Layer\nBuild and tune the datasets, models and measures that reporting runs on, and work with analysts and application developers to get them in front of the people who act on them.\nReporting backed by fast refresh and metrics that reconcile\nShared datasets reused rather than duplicated per report\nBusiness logic implemented once and shared, not repeated per report\nSource Control, Deployment and Support\nWork reaches production through source control and a pipeline: small changes, reviewed before merge, nothing edited in place. Monitor what you shipped and support it, including a share of cover for the pipelines your team owns\nChanges deployed from source control rather than edited in the portal\nPipeline failures alerted, triaged and closed to root cause\nRepeat manual interventions automated away rather than absorbed\nBuilding with Agents, Checked Against Real Data\nSpecify the transformation and the result it must produce, direct coding agents to write it, then verify the output against real data before you trust it. Keep the repository context and the checks current so the next person gets the same leverage\nSpecifications and acceptance criteria written before the build\nGenerated logic verified against known data before release, with the check kept as a test\nRepository context and checks current, and derived from real failures\nRequirements\nAt least 5 years of experience in Data Engineering\nUnderstands data architecture. Real delivery against a Medallion or equivalent layered design, with dimensional modelling judgement and an understanding of data warehousing and ETL or ELT design\nCan build the technology that enables it. Production grade pipelines, advanced SQL including joins, aggregations and incremental loads, and Python or Spark for transformations. Microsoft Fabric is what we run; Databricks, Snowflake or Synapse experience is equally credible.\nHas run what they built. Pipelines you supported in production, with monitoring, refresh reliability and recovery, and the habit of writing them to be replayed safely\nReporting and governance. Power BI dataset modelling, DAX and performance tuning, with metrics that reconcile; data governance, lineage and role based access; and CI/CD for data using Azure DevOps, GitHub Actions or Fabric Git integration.\nKnowledge and awareness of agentic engineering: what these tools are, where they add value and where their output has to be checked against real data. Hands-on experience is beneficial rather than required.","description_format":"text","description_chars":4296,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Philippines","iso":"PH","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-01T18:30:03Z"}],"visa":[],"liveness":{"score":33,"band":"fade","label":"Fading","p_open":1,"p_active":0.366,"p_room":0.9,"age_days":24,"expected_fill_days":50,"reasons":["conf:2","agency","stale_co","velocity","win:mid"],"computed_at":"2026-10-26T05:57:25Z"},"pay":null,"html_url":"https://alion.io/job/manila-recruitment-senior-azure-data-engineer","json_url":"https://alion.io/job/manila-recruitment-senior-azure-data-engineer.json","meta":{"generated_at":"2026-10-26T14:59:41Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":19864,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-27T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1582},"rest":"https://alion.io/mcp/rest/get_company?id=1582"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmanila-recruitment-senior-azure-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fmanila-recruitment-senior-azure-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fmanila-recruitment-senior-azure-data-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/manila-recruitment-senior-azure-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmanila-recruitment-senior-azure-data-engineer"}]}