{"id":135611,"url":"https://alion.io/job/pavago-data-engineer-2","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":192,"ghost_share":0,"stale_share":0.984,"repost_share":0,"time_to_fill_p50_days":32,"computed_at":"2026-10-21T05:46:15Z"}},"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":{"label":"US time zones","utc_offset_min":-8,"utc_offset_max":-5},"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["PK"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Kinesis","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Dagster","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GDPR","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Great Expectations","optional":false},{"name":"HIPAA","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"Power BI","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Terraform","optional":false},{"name":"AWS","optional":true},{"name":"AWS Glue","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"GCP","optional":true},{"name":"Scala","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-09T00:00:00Z","employer_posted_date":"2026-08-09","last_verified_at":"2026-10-21T15:12:16Z","board_verified":true,"closed_at":null,"days_open":73,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":73},"description":"Data Engineer (Python, SQL, ETL, Airflow, Snowflake & BigQuery) - Remote\nPosition Type: Full-Time, Remote\nWorking Hours: U.S. Business Hours\nAbout the Role\nAt Pavago, one of our clients is hiring a highly technical Data Engineer to build, maintain, and optimize scalable data pipelines, cloud data infrastructure, and analytics-ready datasets.\nYou’ll be responsible for the systems that move, transform, validate, and organize data across the business - ensuring analysts, data scientists, engineering teams, and leadership have access to accurate, reliable, and timely data.\nThis is a hands-on engineering role focused on ETL/ELT development, data warehousing, SQL optimization, orchestration, data quality, and cloud infrastructure.\nIf you enjoy building robust data systems, solving complex pipeline problems, and designing infrastructure that scales, this role is for you.\nWhat You’ll Own\nETL / ELT Pipeline Development\nBuild and maintain scalable ETL/ELT pipelines using Python and SQL.\nIngest and process data from:\nAPIs\nSaaS platforms\nRelational databases\nCloud applications\nStreaming systems\nDevelop reliable extraction, transformation, loading, and validation workflows.\nBuild reusable connectors and pipeline components.\nTroubleshoot pipeline failures and data inconsistencies.\nWorkflow Orchestration & Automation\nBuild and manage workflows using Apache Airflow, Prefect, Dagster, Luigi, or similar tools.\nMonitor pipeline health, scheduling, dependencies, and failed jobs.\nImplement automated retries, alerts, and failure handling.\nImprove pipeline reliability and reduce manual intervention.\nMaintain dependable data freshness across critical datasets.\nData Warehousing & Modeling\nDesign and optimize cloud data warehouses using:\nSnowflake\nBigQuery\nRedshift\nBuild analytics-ready data models and warehouse structures.\nDevelop star and snowflake schemas where appropriate.\nOptimize SQL queries and warehouse workloads.\nImprove performance through partitioning, clustering, indexing, and efficient data modeling.\nMonitor and optimize warehouse costs.\nData Quality & Governance\nImplement automated data validation and quality checks.\nBuild monitoring for anomalies, missing data, and transformation failures.\nMaintain logging, lineage, and auditability across pipelines.\nUse tools such as dbt and Great Expectations.\nEstablish consistent naming conventions and transformation standards.\nSupport governance and compliance requirements, including GDPR, HIPAA, or industry-specific standards where applicable.\nStreaming & Real-Time Data\nBuild and maintain streaming or event-driven data pipelines.\nWork with technologies such as:\nKafka\nKinesis\nPub/Sub\nSupport real-time ingestion and low-latency analytics use cases.\nEnsure streaming workflows remain reliable and scalable.\nCloud Infrastructure & DevOps\nContainerize data services using Docker.\nSupport Kubernetes-based environments where applicable.\nBuild and maintain CI/CD workflows using GitHub Actions, Jenkins, GitLab CI, or similar tools.\nSupport infrastructure-as-code using Terraform or CloudFormation.\nImprove deployment reliability, scalability, and automation across the data platform.\nCross-Functional Collaboration\nPartner closely with Data Analysts, Data Scientists, BI teams, Product, and Engineering.\nDeliver curated datasets for:\nDashboards\nBusiness intelligence\nAnalytics\nMachine learning\nOperational reporting\nSupport BI platforms including Tableau, Looker, and Power BI.\nMaintain clear documentation for pipelines, schemas, workflows, and data definitions.\nRequired Experience & Skills\n3+ years of professional Data Engineering or backend engineering experience.\nStrong proficiency in:\nPython\nSQL\nHands-on experience with at least one modern cloud data warehouse:\nSnowflake\nBigQuery\nRedshift\nExperience building and maintaining ETL/ELT pipelines.\nFamiliarity with Airflow, Prefect, or similar orchestration platforms.\nStrong understanding of data modeling and warehouse architecture.\nExperience optimizing SQL queries and data workloads.\nUnderstanding of cloud-based data infrastructure.\nStrong troubleshooting and problem-solving skills.\nExcellent written and verbal English communication.\nNice to Have\nExperience with dbt and Great Expectations.\nExperience with Kafka, Kinesis, or Pub/Sub.\nFamiliarity with AWS Glue, GCP Dataflow, or Azure Data Factory.\nExperience with Docker and Kubernetes.\nInfrastructure-as-code experience using Terraform or CloudFormation.\nExperience building CI/CD pipelines.\nExperience with data lineage and governance tools.\nBackground in healthcare, fintech, or other regulated environments.\nExperience optimizing large-scale warehouse performance and cloud costs.\nScala experience.\nWhat Makes You a Great Fit\nYou care deeply about data accuracy, reliability, and quality.\nYou enjoy debugging complicated pipelines and infrastructure problems.\nYou think beyond getting a pipeline to work - you consider scalability, maintainability, observability, and cost.\nYou write clean, efficient SQL and Python.\nYou proactively identify data quality issues before they reach end users.\nYou understand how engineering decisions affect analysts and business teams downstream.\nYou can communicate technical concepts clearly across technical and non-technical teams.\nWhat a Typical Day Looks Like\nYou may:\nReview Airflow or Prefect pipeline health and investigate failures.\nBuild a new connector for an API or SaaS platform.\nDevelop or improve ETL/ELT workflows.\nOptimize SQL queries and warehouse performance.\nWork with analysts on analytics-ready datasets.\nImprove automated validation and monitoring.\nTroubleshoot data quality or freshness issues.\nReview warehouse usage and identify cost-saving opportunities.\nDocument pipelines, schemas, and data definitions.\nCollaborate with engineering teams on infrastructure improvements.\nIn short: you build and maintain the data infrastructure that powers analytics, reporting, automation, machine learning, and business intelligence.\nKey Metrics for Success\nPipeline uptime of 99%+.\nData freshness consistently maintained within defined SLAs.\nZero critical data quality issues reaching production.\nFaster and more efficient SQL and warehouse performance.\nReduced warehouse and infrastructure costs.\nReliable and scalable ETL/ELT infrastructure.\nStrong monitoring and rapid resolution of pipeline failures.\nHigh confidence in data from Analytics, BI, and leadership teams.\nWhy This Role Stands Out\nWork with modern cloud-native data infrastructure.\nBuild scalable pipelines and analytics systems from end to end.\nGain exposure to data warehousing, orchestration, streaming, and cloud technologies.\nSolve meaningful scalability and reliability challenges.\nCollaborate directly with engineering, analytics, and data science teams.\nHigh technical ownership in a fully remote environment.\nStrong career growth opportunities into:\nSenior Data Engineer\nAnalytics Engineer\nData Platform Engineer\nData Architect\nInterview Process\nInitial Phone Screen\nVideo Interview with Pavago Recruiter\nTechnical Assessment - ETL Pipeline or SQL Optimization Exercise\nClient Interview with Engineering/Data Team\nOffer & Onboarding\nWhat Happens After You Apply\nRight after you apply, you’ll receive an email invitation from Spark Hire to record your Intro Video. This short, self-recorded video completes your application and can be recorded whenever it’s convenient.\nInstead of repeating yourself across multiple screening calls, you’ll introduce yourself once, and your video will be shared with the hiring team. You can record your video as many times as you’d like before submitting it - only your final submission will be reviewed.\nPlease keep an eye on both your inbox and spam folder for your Spark Hire invitation.\nApply Now\nIf you’re a Data Engineer who loves building scalable pipelines, optimizing data infrastructure, and ensuring data stays accurate and reliable, we’d love to hear from you.\nApply today and help build the data systems that power better business decisions.\n#DataEngineer #Python #SQL #ETL #Airflow #Snowflake #BigQuery #DataEngineering #DataPipelines #CloudData #RemoteJobs #DataWarehouse #RemoteWork","description_format":"text","description_chars":8142,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"Upper-Intermediate (B2)","optional":false}]},"benefits":["Growth opportunities"],"hiring_locations":[{"name":"Pakistan","iso":"PK","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Recruiting & Staffing","Employer of Record & PEO"],"lifecycle":[{"event":"open","at":"2026-08-09T00:00:00Z"}],"visa":[],"liveness":{"score":10,"band":"cold","label":"Long shot","p_open":1,"p_active":0.36,"p_room":0.28,"age_days":73,"expected_fill_days":32,"reasons":["conf:0","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-21T05:46:15Z"},"pay":null,"html_url":"https://alion.io/job/pavago-data-engineer-2","json_url":"https://alion.io/job/pavago-data-engineer-2.json","meta":{"generated_at":"2026-10-21T19:28:42Z","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":27610,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-22T00:00:00Z"}},"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":1117},"rest":"https://alion.io/mcp/rest/get_company?id=1117"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fpavago-data-engineer-2"},{"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%2Fpavago-data-engineer-2"},{"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%2Fpavago-data-engineer-2"},{"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/pavago-data-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fpavago-data-engineer-2"}]}