{"id":3357346,"url":"https://alion.io/job/chargebee-lead-data-engineer","title":"Lead Data Engineer","company":{"id":59326,"name":"Chargebee","domain":"chargebee.com","url":"https://alion.io/company/chargebee","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":57000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Apache Kafka","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Java","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Apache Hudi","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"AWS","optional":true},{"name":"Delta Lake","optional":true}],"status":"live","first_seen_at":"2026-10-27T17:29:33Z","employer_posted_date":null,"last_verified_at":"2026-10-27T17:29:33Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"About the Role\n\nThe Data Platform team at Chargebee builds and maintains scalable data systems that power internal analytics, business intelligence, and customer-facing data features\n\nAs a Lead Data Engineer, you will play a key role in shaping the architecture, scalability, and reliability of Chargebee's data platform. You will lead the design and development of large-scale data systems, mentor engineers on the team, and drive best practices across data engineering workflows\n.You will work closely with product engineers, analysts, platform teams, and leadership to ensure that data is ingested, processed, and made available efficiently for analytics and product use cases. This role involves designing robust data pipelines, optimizing distributed data processing systems, and guiding the evolution of the data platform to support Chargebee's growing data needs.\nThe team operates in a fast-paced and collaborative environment, building reliable and scalable infrastructure that powers data-driven decision making across the company\n\nWhat You Will Work On\n\nAs a Lead Data Engineer, you will lead the development and evolution of Chargebee's data platform. This includes designing scalable data architectures, building robust ingestion and processing pipelines, and ensuring data systems operate reliably at sc\nale.You will also guide the technical direction of the platform, mentor engineers, and collaborate across teams to enable efficient and scalable data workflows.\n\nThe role provides exposure to:\n\nLarge-scale data ingestion and processing pipelines\nStreaming and event-driven architectures\nDistributed data processing frameworks\nCloud-based data infrastructure\nBuilding and maintaining data lake and data warehouse architecture.\nDesigning scalable data platforms powering both internal analytics and customer-facing products\nLeading architectural decisions and platform evolution for large-scale data systems\n\nKey Responsibilities\n\nDesign and architect scalable, reliable data ingestion and processing pipelines across the data platform.\nLead the development and optimization of ETL/ELT workflows to support high-volume and scalable data processing.\nBuild and maintain distributed data processing systems using frameworks such as Apache Spark.\nDesign and implement event-driven data architectures using streaming systems such as Kafka.\nDefine data modeling standards and transformation strategies to support analytics and product use cases.\nEnsure high standards of data reliability, integrity, scalability, and performance across the data platform.\nLead troubleshooting and debugging efforts for complex production data pipelines and distributed systems.\nCollaborate with data analysts, product teams, and engineering teams to design scalable data solutions.\nMentor and guide data engineers, providing technical leadership and promoting engineering best practices.\nLead design discussions, architecture reviews, and technical decision-making within the data platform team.\nParticipate in and drive code reviews, technical design reviews, and agile development processes. \nDocument architecture decisions, platform standards, and data engineering workflows.\n\nMinimum Qualifications\nBachelor's degree in Computer Science, Mathematics, Engineering, or a related technical field, or equivalent practical experience.\n8+ years of experience building and maintaining large-scale data processing systems and pipelines.\nStrong experience designing and operating production-grade distributed data systems.\nHands-on experience with distributed computing frameworks such as Apache Spark.\nStrong proficiency in SQL and data modeling.\nProficiency in at least one programming language such as Java, Python, or Scala.\nStrong understanding of data structures, distributed systems, and data platform architecture.\nExperience working with relational databases such as PostgreSQL, MySQL, or similar systems.\nExperience designing and building ETL/ELT data pipelines at scale.\nExperience working with Git workflows in collaborative development environments.\nExperience working in an Agile development environment.\n\nGood-to-Have Qualifications\n\nExperience working within the AWS ecosystem.\nExperience building and operating large-scale Apache Spark-based data pipelines.\nExperience with streaming systems such as Kafka or similar event-driven platforms.\nStrong understanding of data lake and data warehouse architectures\nExperience designing scalable cloud-native data platforms.\nKnowledge or prior experience with open table formats such as Delta Lake, Apache Iceberg, or Apache Hudi.\nStrong technical leadership, communication, and problem-solving skills.\nAbility to investigate and debug issues across large-scale distributed systems.\n\nSkills\nJava, Git, MySQL, Scala, PostgreSQL, Apache Spark, Kafka, Sql, Python, Etl, 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