{"id":1644755,"url":"https://alion.io/job/accelone-data-engineer","title":"Data Engineer","company":{"id":2238225,"name":"AccelOne","domain":"accelone.com","url":"https://alion.io/company/accelone","size_band":"51-200","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":{"grade":"D","score":52,"open_postings":10,"ghost_share":0.8,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-05T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Buenos Aires, Argentina"],"countries":["AR"],"hiring_countries":["AR"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":41000,"max_usd":104000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1839},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BigQuery","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-04-29T18:18:26Z","employer_posted_date":"2026-04-29","last_verified_at":"2026-10-06T00:19:15Z","board_verified":true,"closed_at":null,"days_open":159,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":158},"description":"AI & Data Center of Excellence - Abu Dhabi, UAE\nRole Overview\nAs a Data Engineer, you will be responsible for building and maintaining scalable, reliable, and secure data platforms that power analytics and AI use cases across the organization.\nThis role is critical in enabling data-driven decision-making in financial services environments. You will work closely with data scientists, AI engineers, and business stakeholders to ensure data systems are robust, performant, and aligned with regulatory and operational requirements.\nExperience Bands\nSenior Data Engineer: 8-10 years of experience\nData Engineer: 5-7 years of experience\nKey Responsibilities\nDesign and implement robust ETL/ELT pipelines for structured and unstructured data\nBuild and manage scalable data lakes, data warehouses, and real-time data pipelines\nEnsure data quality, lineage, governance, and compliance across data platforms\nEnable reliable data availability for analytics, reporting, and AI systems\nOptimize data infrastructure for performance, scalability, and cost efficiency\nCollaborate with Data Science and AI teams to productionize machine learning pipelines\nMonitor and troubleshoot data workflows and system performance\nImplement best practices for data security and reliability\nFinancial Services Use Cases (Preferred)\nCandidates with experience in financial data environments will be highly valued, particularly in:\nTransaction data pipeline development and management\nRegulatory reporting and compliance data systems\nRisk and finance data marts\nCustomer 360 and customer analytics platforms\nTechnical Skills\nData Platforms\nSnowflake\nBigQuery\nAmazon Redshift\nDatabricks\nData Processing Technologies\nApache Spark\nApache Kafka\nApache Flink\nDatabases\nSQL databases\nNoSQL databases\nDevOps & Engineering Practices\nCI/CD pipelines\nVersion control systems (e.g., Git)\nContainers & Infrastructure\nDocker\nKubernetes\nCloud Platforms\nAWS\nAzure\nGoogle Cloud Platform (GCP)\nEvaluation Criteria\nCandidates will be evaluated based on:\nComplexity and scale of data systems built and maintained\nReliability and performance of data pipelines in production environments\nExperience implementing data governance and compliance standards\nExposure to AI and machine learning data pipelines\nAbility to design scalable and resilient data architectures\nKey Performance Indicators (KPIs)\nReliability of data pipelines (uptime, failure rate)\nData latency and freshness\nData quality and integrity metrics\nCost optimization and efficiency of data infrastructure\nStability and scalability of data platforms\nPreferred Profile\nExperience working within financial data ecosystems\nUnderstanding of regulatory data requirements and compliance standards\nExposure to MLOps and machine learning data pipelines\nExperience working in distributed or cross-functional teams\nStrong problem-solving and ownership 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