{"id":2093856,"url":"https://alion.io/job/inseego-data-analytics-engineer","title":"Data Analytics Engineer","company":{"id":680065,"name":"Inseego","domain":"inseego.com","url":"https://alion.io/company/inseego","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Wi-Fi","optional":false},{"name":"Azure","optional":true},{"name":"dbt","optional":true},{"name":"Delta Lake","optional":true},{"name":"Informatica","optional":true},{"name":"Microsoft Fabric","optional":true},{"name":"MS SQL","optional":true},{"name":"Power BI","optional":true},{"name":"pySpark","optional":true},{"name":"Python","optional":true},{"name":"Rest API","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-08T08:13:52Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-11T16:53:59Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Inseego is a global leader in wireless broadband, delivering fast, reliable connectivity to homes, businesses, and people on the move. With the acquisition of Nokia’s FastMile fixed wireless access product, Inseego offers one of the industry’s broadest cellular broadband portfolios, spanning fixed wireless gateways, mobile hotspots and routers, enterprise and industrial gateways, and cloud-based device, network, and subscriber management software. Backed by decades of wireless engineering expertise across 5G, Wi-Fi, antenna design, and cloud software, Inseego partners with mobile network operators worldwide to deliver connectivity at scale. Headquartered in San Diego, California, Inseego has offices in Amsterdam, Bangalore, and Athens.\nPreferred Qualifications :\nMicrosoft Fabric certification (DP-600 or equivalent) or Azure Data Engineer Associate (DP-203) \nDirect experience with Microsoft Fabric (OneLake, Lakehouse, Warehouse, Pipelines, Dataflows Gen2, Direct Lake) \nExperience extracting data from Oracle NetSuite (ODBC, SuiteAnalytics Connect, REST API, or CSV exports) \nExperience with Salesforce data extraction (Bulk API, Tableau CRM, or CRM Analytics) \nFamiliarity with dbt (data build tool) for SQL-based transformation layer \nKnowledge of Delta Lake / Apache Parquet file formats \nExperience with Informatica or Celigo as upstream integration/data sources \nExposure to Rippling or Arena data structures for HR and product data analytics Technical Stack & Tools \nAnalytics Platform: Microsoft Fabric (OneLake, Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2) \nVisualization: Power BI (Direct Lake, semantic models, DAX) \nERP Source: Oracle NetSuite (SuiteAnalytics Connect / REST API) \nCRM Source: Salesforce (Bulk API / connected app) \nHRIS Source: Rippling \nPLM Source: Arena • Integration Layer: Informatica / maybe move to Celigo(upstream data provider) \nLanguages: SQL (T-SQL / Spark SQL), Python / PySpark, DAX \nStorage: Delta Lake / OneLake (Parquet) \nOptional: dbt for transformation layer\nPreferred Qualifications :\nMicrosoft Fabric certification (DP-600 or equivalent) or Azure Data Engineer Associate (DP-203) \nDirect experience with Microsoft Fabric (OneLake, Lakehouse, Warehouse, Pipelines, Dataflows Gen2, Direct Lake) \nExperience extracting data from Oracle NetSuite (ODBC, SuiteAnalytics Connect, REST API, or CSV exports) \nExperience with Salesforce data extraction (Bulk API, Tableau CRM, or CRM Analytics) \nFamiliarity with dbt (data build tool) for SQL-based transformation layer \nKnowledge of Delta Lake / Apache Parquet file formats \nExperience with Informatica or Celigo as upstream integration/data sources \nExposure to Rippling or Arena data structures for HR and product data analytics\nTechnical Stack & Tools :\nAnalytics Platform: Microsoft Fabric (OneLake, Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2) \nVisualization: Power BI (Direct Lake, semantic models, DAX) \nERP Source: Oracle NetSuite (SuiteAnalytics Connect / REST API) \nCRM Source: Salesforce (Bulk API / connected app) \nHRIS Source: Rippling \nPLM Source: Arena\nIntegration Layer: Informatica / maybe move to Celigo(upstream data provider) \nLanguages: SQL (T-SQL / Spark SQL), Python / PySpark, DAX \nStorage: Delta Lake / OneLake (Parquet) \nOptional: dbt for transformation layer\nEducation:\nBachelor in Electronic and Communication or related 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