{"id":4202761,"url":"https://alion.io/job/latentview-analytics-assistant-manager-data-engineering","title":"Assistant Manager - Data Engineering","company":{"id":63513,"name":"LatentView Analytics","domain":"latentview.com","url":"https://alion.io/company/latentview-analytics","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Leadership","role_family":"Leadership","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-11-12T22:35:00Z","employer_posted_date":null,"last_verified_at":"2026-11-12T22:35:00Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"We are seeking an experienced Data Engineer with hands-on experience in ETL development and AWS-based data engineering solutions. The ideal candidate will have strong expertise in PySpark, Python (NumPy, Pandas), and large-scale distributed data processing, along with proven experience in deploying and managing AWS data services.\n\nYou will work closely with IT and business stakeholders to design, build, and optimize scalable data pipelines while ensuring cost efficiency, data quality, and high performance across the data ecosystem.\n\nKey Responsibilities:\nDesign, develop, and maintain scalable ETL/ELT pipelines using PySpark and Python.\nBuild and deploy AWS-based data solutions leveraging services such as Glue, S3, Lambda, SNS, and Step Functions.\nPerform large-scale data transformations and processing using distributed data frameworks.\nImplement and manage Delta Lake/Delta Tables within a Data Lake architecture.\nOptimize data pipelines for performance, scalability, and cost efficiency in AWS environments.\nEnsure data quality, reliability, and governance across integrated data systems.\nCollaborate with cross-functional IT and business stakeholders to resolve delivery issues and align on requirements.\nOrchestrate workflows using MWAA (Airflow) and manage end-to-end data pipeline automation is an added advantage.\n\nRequired Skills:\n4 to 8 years of experience in Data Engineering.\nStrong expertise in AWS services (S3, Lambda, SNS, Step Functions).\nHands-on experience with PySpark and Python.\nStrong proficiency in Python libraries such as NumPy and Pandas.\nExperience with large-scale data transformations.\nStrong communication and stakeholder management skills.\n\nGood to Have:\nMetadata management, data lineage, data governance, and Data Lake architecture.\nDelta Tables / Delta Lake and MWAA (Airflow orchestration).\nExperience in cost optimization strategies in AWS.\nSkills\nAWS, Airflow, Data Engineering, Glue, Lambda, PySpark, Python, 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