{"id":47432,"url":"https://alion.io/job/forbes-advisor-lead-data-engineer","title":"Lead Data Engineer","company":{"id":8272,"name":"Forbes","domain":"forbes.com","url":"https://alion.io/company/forbes-advisor","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":87,"open_postings":3,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-05T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":50000,"period":"year","method":"role_seniority_country_cell","sample_n":16},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Apache Kafka","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Looker","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-08-08T09:42:47Z","employer_posted_date":null,"last_verified_at":"2026-08-08T09:42:47Z","board_verified":false,"closed_at":null,"days_open":58,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":58},"description":"We're hiring a Data Engineering Lead to help scale and guide a growing team of data engineers. This role is ideal for someone who enjoys solving technical challenges hands-on while also shaping engineering best practices, coaching others, and helping cross-functional teams deliver data products with clarity and speed. You'll manage a small team of ICs responsible for building and maintaining pipelines that support reporting, analytics, and machine learning use cases. You'll be expected to drive engineering excellence from code quality to deployment hygiene and play a key role in sprint planning, architectural discussions, and stakeholder collaboration. This is a critical leadership role as our data organization expands to meet growing demand across media performance, optimization, customer insights, and advanced analytics.\nResponsibilities:\nLead and grow a team of data engineers working across ETL/ELT, data warehousing, and ML-enablement.\nOwn team delivery across sprints, including planning, prioritization, QA, and stakeholder communication.\nSet and enforce strong engineering practices around code reviews, testing, observability, and documentation.\nCollaborate cross-functionally with Analytics, BI, Revenue Operations, and business stakeholders in Marketing and Sales.\nGuide technical architecture decisions for our pipelines on GCP (BigQuery, GCS, Composer).\nModel and transform data using dbt and SQL, supporting reporting, attribution, and optimization needs.\nEnsure data security, compliance, and scalability, especially around first-party customer data.\nMentor junior engineers through code reviews, pairing, and technical roadmap discussions.\nRequirements:\n6+ years of experience in data engineering, including 2+ years of people management or formal team leadership.\nStrong technical background with Python, Spark, Kafka, and orchestration tools like Airflow.\nDeep experience working in GCP, especially BigQuery, GCS, and Composer.\nStrong SQL skills and familiarity with DBT for modeling and documentation.\nClear understanding of data privacy and governance, including how to safely manage and segment first-party data.\nExperience working in agile environments, including sprint planning and ticket scoping.\nExcellent communication skills and proven ability to work cross-functionally across global teams.\nNice to have:\nExperience leading data engineering teams in digital media or performance marketing environments.\nFamiliarity with data from Google Ads, Meta, TikTok, Taboola, Outbrain, and Google Analytics (GA4).\nExposure to BI tools like Tableau or Looker.\nExperience collaborating with data scientists 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