{"id":947524,"url":"https://alion.io/job/pavago-data-engineer-4","title":"Data Engineer","company":{"id":1117,"name":"Pavago","domain":"pavago.co","url":"https://alion.io/company/pavago","size_band":null,"is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":75,"open_postings":201,"ghost_share":0,"stale_share":0.99,"repost_share":0,"time_to_fill_p50_days":33,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","CR"],"hiring_countries_total":3,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Redshift","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Kubernetes","optional":false},{"name":"Looker","optional":false},{"name":"Power BI","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS Glue","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"dbt","optional":true},{"name":"Spark","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-09-15T21:44:49Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-11-03T12:32:18Z","board_verified":true,"closed_at":null,"days_open":48,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":48},"description":"Data Engineer - Remote\nPosition Type: Full-Time, Remote\nWorking Hours: U.S. Client Business Hours (flexibility for pipeline monitoring, deployments, and data refresh cycles)\nAbout the Role\nAt Pavago, one of our clients is hiring a Data Engineer to design, build, and maintain scalable data infrastructure that powers analytics, reporting, and business decision-making.\nYou’ll build reliable data pipelines, optimize data warehouses, ensure data quality, and collaborate with engineering, analytics, and business teams to deliver trusted, high-performance data solutions.\nWhat You’ll Do\nData Pipelines & Integration\nBuild and maintain ETL/ELT pipelines using Python, SQL, or Scala.\nOrchestrate workflows with Airflow, Prefect, Dagster, or similar tools.\nIntegrate data from APIs, databases, SaaS platforms, files, and streaming sources.\nDevelop scalable data ingestion workflows.\nData Warehousing & Modeling\nManage cloud data warehouses such as Snowflake, BigQuery, or Redshift.\nDesign scalable data models and schemas.\nOptimize warehouse performance through partitioning, clustering, and indexing.\nBuild analytics-ready datasets for reporting and BI.\nData Quality & Governance\nImplement validation, monitoring, and anomaly detection.\nMaintain documentation, lineage, and data governance standards.\nEnsure reliable, audit-ready data processes.\nMonitor pipeline health and resolve failures proactively.\nStreaming & Real-Time Processing\nBuild and maintain real-time data pipelines.\nSupport event-driven architectures and streaming platforms.\nOptimize performance and reliability of streaming workflows.\nCollaboration & Analytics\nPartner with analysts, data scientists, and business teams.\nSupport reporting initiatives in Power BI, Tableau, Looker, or similar platforms.\nTranslate business requirements into scalable data solutions.\nDocument pipelines, workflows, and data models.\nInfrastructure & Automation\nDeploy data services using Docker and Kubernetes.\nSupport CI/CD pipelines and cloud infrastructure.\nImprove system scalability, reliability, and cost efficiency.\nRequired Experience & Skills\n3+ years of experience in Data Engineering, Data Infrastructure, or Back-End Engineering.\nStrong Python and SQL skills.\nExperience with Snowflake, BigQuery, Redshift, or similar cloud data warehouses.\nHands-on experience with Airflow, Prefect, or similar orchestration tools.\nStrong understanding of ETL/ELT pipelines and data modeling.\nExperience with AWS, Azure, or Google Cloud.\nNice to Have\nExperience with dbt.\nKafka, Kinesis, Pub/Sub, or other streaming platforms.\nAWS Glue, GCP Dataflow, or Azure Data Factory.\nDocker, Kubernetes, Terraform, or CI/CD pipelines.\nExperience in healthcare, fintech, SaaS, or other regulated industries.\nExperience optimizing warehouse performance and cloud costs.\nWhat a Typical Day Looks Like\nMonitor pipeline health and troubleshoot failures.\nBuild and maintain data ingestion pipelines.\nOptimize SQL queries and warehouse performance.\nDeliver reliable datasets for analytics and reporting.\nImplement monitoring and data quality checks.\nDocument pipelines and data models.\nIn short: You’ll build and maintain reliable data infrastructure that enables accurate reporting, analytics, and business decisions.\nKey Metrics for Success\n99%+ pipeline uptime.\nData freshness maintained within SLA targets.\nHigh data quality with minimal downstream issues.\nImproved warehouse performance and cost optimization.\nReliable delivery of scalable datasets.\nStrong stakeholder satisfaction.\nInterview Process\nApplication Review\n Spark Hire Intro Video (3-5 minutes)\nTechnical Assessment (ETL Pipeline or SQL Exercise)\nClient Interview\nOffer & Onboarding\nWhat Happens After You Apply\nAfter submitting your application, you’ll receive an email invitation from Spark Hire to record a short 3-5 minute Intro Video. This is the first step in our hiring process and can be completed whenever it’s convenient for you.\nInstead of multiple screening calls, you’ll have one opportunity to introduce yourself, discuss your data engineering experience, and highlight your background in data pipelines, cloud platforms, SQL, Python, and data warehousing. Your video will be reviewed before moving forward in the interview process.\nYou can record your video as many times as you’d like before submitting it-only your final version will be reviewed.\nPlease keep an eye on both your inbox and spam folder for your Spark Hire invitation after applying.\nApply Now\nIf you enjoy building scalable data pipelines, optimizing cloud data platforms, and delivering reliable data that drives business decisions, we’d love to hear from you. 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