{"id":2638058,"url":"https://alion.io/job/ascendion-senior-data-engineer-2","title":"Senior Data Engineer","company":{"id":46972,"name":"Ascendion","domain":"ascendion.com","url":"https://alion.io/company/ascendion","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":81,"open_postings":33,"ghost_share":0,"stale_share":0.758,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Chennai, India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":59},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"CI/CD","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Waterfall","optional":false},{"name":"Airflow","optional":true},{"name":"Apache Iceberg","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Spark","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-13T12:17:38Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-16T22:58:26Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"Job Description: Snowflake Data Engineer - Python, PySpark & AWS\nJob Overview\nWe are seeking a skilled Snowflake Data Engineer with strong expertise in Snowflake, Python, PySpark, and SQL to design, develop, optimize, and maintain scalable data pipelines and data warehousing solutions. The ideal candidate will have hands-on experience with AWS, GitHub, and modern data engineering practices, along with a good understanding of machine learning, AI development tools, and emerging software development methodologies.\nThe candidate should possess strong analytical and problem-solving skills, a solid understanding of ETL/ELT processes, and the ability to collaborate with cross-functional teams to deliver high-quality, reliable, and efficient data solutions.\nKey Responsibilities\nDesign, develop, and maintain scalable ETL/ELT processes and data pipelines using Python, PySpark/Snowpark, SQL, and Snowflake.\nBuild, optimize, and manage data processing workflows and data transformation solutions on AWS, including Amazon S3.\nDevelop efficient SQL queries, stored procedures, and data transformation logic, applying performance optimization techniques.\nImplement and maintain data warehousing solutions using dimensional modeling, normalized data structures, and industry-standard data modeling practices.\nWork with GitHub and related development tools to manage source code, collaborate with team members, and support version control and CI/CD processes.\nDevelop and maintain data quality checks, monitoring mechanisms, and error-handling processes to ensure data accuracy, consistency, and reliability.\nCollaborate with technical teams, business stakeholders, and internal and external technology resources to gather requirements and finalize technical specifications.\nWrite, review, and maintain detailed technical specifications for complex system components.\nTroubleshoot and resolve complex technical issues, perform root-cause analysis, and implement effective bug fixes.\nApply critical thinking and research skills to identify opportunities for improving data processing efficiency, scalability, and system performance.\nLeverage AI development tools and explore modern development approaches, including Spec-Driven Development (SDD) and AWS AI-DLC.\nContribute to Agile or Waterfall development processes, ensuring adherence to coding standards, development best practices, and quality expectations.\nSupport the integration of machine learning models and statistical analysis techniques into data engineering workflows, where applicable.\nRequired Skills and Qualifications\nStrong hands-on expertise in Snowflake, Python, PySpark, and SQL.\nExperience with PySpark and/or Snowpark for large-scale data processing and transformation.\nProven experience developing and maintaining ETL/ELT workflows and data pipelines.\nHands-on experience with AWS services, particularly Amazon S3.\nPractical experience using GitHub for version control and collaborative development.\nStrong understanding of relational databases, data warehousing concepts, and ETL processes.\nAdvanced SQL skills, including query optimization, performance tuning, and complex data transformations.\nKnowledge of normalized and dimensional data modeling principles.\nExperience with data quality, validation, monitoring, and error-handling practices.\nFamiliarity with software development methodologies, including Agile and Waterfall.\nExperience with machine learning models or statistical analysis.\nExcellent analytical, critical-thinking, debugging, and problem-solving skills.\nStrong communication and collaboration skills, with the ability to work effectively with technical teams and stakeholders.\nAbility to understand complex requirements, contribute to technical design, review specifications, and deliver reliable software components.\nPreferred / Good-to-Have Skills\nExperience with Apache Iceberg and modern open table formats.\nKnowledge of Apache Airflow or other workflow orchestration tools.\nExperience with Terraform or other Infrastructure as Code (IaC) tools.\nFamiliarity with GitHub Actions and CI/CD pipeline implementation.\nKnowledge of advanced data modeling and data warehousing techniques.\nFamiliarity with AI-assisted software development tools.\nUnderstanding of Spec-Driven Development (SDD) and AWS AI-DLC.\nExposure to machine learning workflows and statistical analysis in data-intensive environments.\nHands-on experience with Jetstream.\nPrimary Skills\nPython\nPySpark / Snowpark\nSQL\nSnowflake\nETL/ELT and Data Pipeline Development\nAWS / Amazon S3\nSecondary Skills\nApache Iceberg\nApache Airflow\nTerraform / Infrastructure as Code\nGitHub Actions / CI/CD\nData Modeling and Data Warehousing\nData Quality and Monitoring\nAI Development Tools\nSpec-Driven Development (SDD) / AWS AI-DLC\nOur Commitment to Inclusion\nWe welcome applications from individuals of all backgrounds, including underrepresented groups, people with disabilities, career changers, and candidates with non-traditional education or experience.\nIf you require accommodations at any stage of the recruitment process, please let us know. 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