{"id":3681657,"url":"https://alion.io/job/nagad-senior-executive-core-data-engineering","title":"Senior Executive - Core Data Engineering","company":{"id":180378,"name":"Nagad","domain":"nagad.com.bd","url":"https://alion.io/company/nagad","size_band":null,"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":"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":["Dhaka, Bangladesh"],"countries":["BD"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Core Data","optional":false},{"name":"Dagster","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Flink","optional":false},{"name":"Kubernetes","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-03T09:41:00Z","employer_posted_date":null,"last_verified_at":"2026-11-03T09:41:00Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Work on Nagad`s core data platforms powering fintech operations. Gain exposure to large-scale transactional & analytical systems. Ensure data accuracy, integrity & reliability. Fast-paced growth, competitive pay & benefits.\n\nResponsibilities & ContextDesign, develop, and maintain scalable batch and near real-time data pipelines to support analytics, reporting and advanced use cases.\nDefine and implement data architecture standards including layered data models for raw, refined, and curated datasets.\nBuild and manage end-to-end data ingestion, transformation, and delivery workflows across multiple data sources.\nEnsure consistency, quality, reliability, and performance across all data pipelines.\nDesign and maintain analytics-ready and ML-ready datasets, including structured and semi-structured data.\nEstablish and maintain data lineage, traceability, and documentation from source to consumption.\nImplement effective data orchestration, dependency management, and workflow scheduling.\nDesign and maintain dimensional data models to support BI, analytics, and reporting needs.\nCollaborate with BI, Analytics, AI/ML, and Assurance teams to align data outputs with business requirements.\nEnsure adherence to data governance, security, and access control standards.\n\nEducationBachelor’s degree in Computer Science or relevant subjects with very good command over programming skills.\n\nExperienceAt least 3 years\nThe applicants should have experience in the following business area(s): Financial Technology (Fintech) Startup\n\nAdditional RequirementsSkills Requirement:\nStrong proficiency in Python, Scala, and SQL for large-scale data processing, transformation, and analytics.\n\nHands-on experience with distributed data processing like (Apache Spark/ Flink & Kafka) and streaming frameworks for batch and near real-time pipelines.\n\nSolid understanding of ETL / ELT concepts, data transformation practices and analytics engineering (e.g., dbt)\n\nStrong knowledge of data lake and data warehouse architecture including medallion (Bronze / Silver / Gold) models\n\nExperience working with modern table formats, metadata, and catalog-driven storage (e.g., Parquet, Iceberg)\n\nHands-on experience with distributed and object storage systems (e.g., HDFS, S3 or equivalent)\n\nExperience integrating relational and NoSQL databases and handling semi-structured data (Oracle, PostgreSQL, MongoDB, JSON)\n\nProficiency in data orchestration and workflow management (Airflow, Dagster, Mage AI or equivalent)\n\nExperience with containerization and deployment using Docker and Kubernetes\n\nProficiency in shell scripting for automation and operational support\n\nSkillsApache Spark, Big Data Handling, Data processing, Data Warehousing, Dockers and Kubernetes, ETL, Kafka, Python, Shell Scripting, SQL\n\nCompensation & Other BenefitsProvident fund,Mobile bill,Insurance,Weekly 2 holidays\nSalary Review: Yearly\nFestival Bonus: 2\n\nEmployment status: Full Time\nWorkplace: Work at office\nVacancies: 1\nSalary: Negotiable\nJob location: 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