{"id":3281777,"url":"https://alion.io/job/falabella-senior-data-engineer","title":"Senior Data Engineer","company":{"id":3271,"name":"Falabella","domain":"falabella.com","url":"https://alion.io/company/falabella","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":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":{"min_usd":22000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Flink","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"GitHub","optional":true},{"name":"GitLab","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Google Bigtable","optional":true},{"name":"Google GKE","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-10-26T16:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T16:35:00Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Responsibilities: \nDesign, build, and maintain scalable batch and streaming data pipelines on GCP.\nIngest data from multiple sources, including traditional databases and API-based integrations, into our cloud data platform.\nDevelop data and feature engineering workflows that support analytics, BI, and machine learning use cases.\nOrchestrate and schedule pipelines with tools such as Cloud Composer/Airflow and Vertex AI Pipelines.\nContribute to data architecture design, choosing the right storage and compute options for each use case.\nOptimize pipeline and cluster performance for reliability, scalability, and cost.\nPackage and deploy data workloads with Docker and CI/CD practices.\nWork with data scientists, analysts, and business teams to understand how data is used and ensure it is accurate, available, and fit for purpose.\n\nRequirements: \nStrong programming skills in Python, SQL, and PySpark.\nHands-on experience with at least one cloud platform (GCP preferred).\nExperience working with Docker.\nExperience managing environments and dependencies (e. g., venv, pip).\nExperience with orchestrators such as Vertex AI Pipelines or Airflow.\nSolid knowledge of data engineering and feature engineering techniques.\nProficiency in at least one of Apache Spark, Apache Beam, or Apache Flink.\nUnderstanding of streaming concepts such as windowing, late-arriving data, and triggers.\n\nShould have: \nHands-on experience with distributed computing.\nWorking experience in data architecture design.\nKnowledge of storage and compute options and when to choose each.\nGood understanding of cluster and pipeline optimization strategies.\nExperience using GCP tools to build end-to-end data pipelines for different scenarios, including ingestion from traditional databases and API-based sources.\nExperience with CI/CD pipelines and deployment methodologies.\nA business mindset: understanding the data and how it will be used for BI and analytics.\nGood to have: Hands-on experience with Kubernetes.\n\nTools and Technologies (GCP): \nStorage: Cloud SQL, Cloud Storage, Cloud Bigtable, BigQuery.\nIngestion: Pub/Sub, Cloud Functions, Google Kubernetes Engine (GKE).\nScheduling and Orchestration: Cloud Composer, Airflow, and Vertex AI Pipelines.\nProcessing: Cloud Dataproc, Cloud Dataflow, Apache Spark.\nDevOps: Docker, CI/CD (e. g., GitHub/GitLab Actions).\nSkills\nApache Spark, PySpark, Python, SQL, 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