{"id":3361292,"url":"https://alion.io/job/chart-metric-data-engineer","title":"Data Engineer","company":{"id":675127,"name":"Chart Metric","domain":"chartmetric.com","url":"https://alion.io/company/chartmetric","size_band":"11-50","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":"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":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":88000,"max_usd":187000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":376},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"ClickHouse","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-27T17:02:28Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-31T13:29:46Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"About Chartmetric\nChartmetric, Inc. is a 10-year-old startup specializing in music data analytics. We are trusted by Universal, Sony, Warner, and Apple Music, as well as hundreds of other music companies and industry professionals. Our team has created a self-service data dashboard for the music industry to better understand the activity happening around artists. Together, we combine hundreds of thousands of real-time data points across iTunes, Spotify, YouTube, Google, Amazon, X, and others through our beautifully designed tool in order to make sense of the increasingly complex landscape of the music industry.\nWhat you'll do\nBuild and run Python ingestion pipelines that pull data from streaming platforms, social networks, and third-party data vendors, orchestrated in Apache Airflow\nWork with data for 10M+ artists, 100M+ tracks, and 15M+ playlists, plus fast-growing datasets for creators, brands, and athletes\nModel and serve data in ClickHouse and Snowflake, and help move our pipelines from Snowflake to S3 and ClickHouse\nBuild and maintain data shares and deliveries for enterprise customers, including major labels and publishers\nKeep data fresh and correct: data monitoring, data quality checks, etc.\nWork closely with senior engineers, product, and data science on new data products, using AI coding tools as a normal part of your workflow\nThis position is hybrid, in our San Mateo or New York office 3-4 days a week\nQualifications\n2-4 years of experience building production data pipelines, preferably in high-volume consumer or entertainment industries\nStrong Python and SQL, with hands-on experience in Apache Airflow (or a similar orchestrator), a columnar warehouse such as ClickHouse or Snowflake, and AWS\nExperience building and running pipelines that handle millions of records a day, including messy third-party APIs (rate limits, retries, changing schemas)\nStrong communication skills with ability to translate complex technical concepts to cross-functional stakeholders\nBachelor's degree in Computer Science, Data Engineering, or equivalent practical experience\nThrives in dynamic startup environments with rapidly evolving requirements and tight delivery timelines\nBonus: Experience with music industry data, streaming APIs, or social media analytics platforms\nWhat We Offer\nCompetitive salary and equity package\nComprehensive health, dental, and vision insurance\nReal ownership of pipelines that customers use every day\nAccess to cutting-edge tools and technologies\nWhat We Offer\nWe believe that great developer experiences lead to better products and happier teams. 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