{"id":3468460,"url":"https://alion.io/job/netrio-senior-data-engineer","title":"Data Engineer","company":{"id":678445,"name":"Netrio","domain":"netrio.com","url":"https://alion.io/company/netrio","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Rippling","truth_index":{"grade":"A","score":88,"open_postings":7,"ghost_share":0,"stale_share":0.286,"repost_share":0,"time_to_fill_p50_days":69,"computed_at":"2026-11-04T06:34:25Z"}},"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":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Java","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"dbt","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-16T20:28:48Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-11-04T07:33:14Z","board_verified":true,"closed_at":null,"days_open":18,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":18},"description":"About Netrio\nNetrio is a leading MSP in North America, specializing in IT solutions for small- to mid-market enterprises. We serve over 1,000 clients across industries with services including managed IT, cybersecurity, cloud, connectivity, voice, and custom application development.\nAbout the role\nWe are seeking a Data Engineer to design, build, and optimize modern data platforms that enable analytics, reporting, operational intelligence, and AI-driven solutions.\nIn this role, you will work across the full data lifecycle, developing scalable data pipelines, integrating complex data sources, and delivering high-quality data solutions that support business and customer objectives. You will collaborate with architects, engineers, analysts, and stakeholders to modernize data environments and implement cloud-based data platforms.\nThe ideal candidate is both a strong technical practitioner and a problem solver who enjoys working with modern data technologies, navigating ambiguity, and delivering scalable solutions in dynamic environments.\nWhat you'll do\nServe as the technical leader for our data engineering initiatives.\nDesign, develop, and maintain scalable data pipelines that ingest, transform, and deliver data from structured, semi-structured, and unstructured sources.\nBuild and support ETL/ELT solutions across cloud-based data platforms, data warehouses, lakehouses, and analytics environments.\nDevelop data integration frameworks that enable reliable, secure, and efficient movement of data across systems.\nDesign and implement dimensional models, transformation logic, and data structures that support analytics, reporting, and AI initiatives.\nWork with modern cloud data technologies such as Microsoft Fabric, Databricks, Snowflake, and related platforms.\nCollaborate with architects and stakeholders to translate business requirements into scalable technical solutions.\nApply data quality, testing, monitoring, lineage, and governance best practices throughout the development lifecycle.\nOptimize data pipelines and data platforms for performance, scalability, reliability, and cost efficiency.\nSupport modernization initiatives involving legacy data platforms, data warehouses, and integration technologies.\nImplement modern engineering practices including source control, automated testing, deployment automation, and CI/CD processes.\nTroubleshoot and resolve complex technical issues across data platforms and integration environments.\nContribute to architecture reviews, solution design discussions, and continuous improvement initiatives.\nStay current with evolving technologies, industry trends, and emerging approaches within data engineering, analytics, and AI.\nQualifications\n6+ years of experience in Data Engineering, Data Integration, Analytics Engineering, Software Engineering, or a related technical discipline.\nStrong experience designing, building, and supporting production-grade ETL/ELT pipelines and data integration solutions.\nAdvanced SQL skills, including query optimization, performance tuning, and data modeling.\nExposure to cloud platforms such as AWS, Azure, or GCP\nSolid experience developing data solutions using Python, Scala, Java, or similar programming languages.\nExperience working with cloud-based data platforms, data warehouses, lakehouses, and modern analytics environments.\nExperience with distributed data processing technologies such as Spark or equivalent frameworks.\nHands-on experience with modern data platforms such as Microsoft Fabric, Databricks, Snowflake, BigQuery, Redshift, or similar technologies.\nFamiliarity with Git-based development workflows and CI/CD practices.\nUnderstanding of data quality, governance, lineage, security, and observability concepts.\nAbility to communicate effectively with both technical and non-technical stakeholders.\nStrong analytical, troubleshooting, and problem-solving skills.\nAbility to adapt to changing priorities, learn new technologies quickly, and succeed in fast-paced environments.\nPreferred Qualifications\nExperience implementing solutions using Microsoft Fabric, Databricks and Snowflake in production environments.\nExperience with Databricks and modern lakehouse architectures.\nExperience with dbt or similar transformation frameworks.\nExperience modernizing enterprise data warehouses and legacy integration environments.\nExperience with Azure Data Services or equivalent cloud-native data technologies.\nExperience supporting machine learning, artificial intelligence, or advanced analytics initiatives.\nFamiliarity with semantic modeling, analytics-ready data structures, and data product 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