{"id":3276163,"url":"https://alion.io/job/m-league-sr-data-engineer","title":"Sr. Data Engineer","company":{"id":9447,"name":"M-League","domain":"mleague.gg","url":"https://alion.io/company/m-league","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":21000,"max_usd":46000,"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":"Apache Kafka","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-26T14:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-26T14:35:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"We are looking for a Senior Data Engineer (SDE 2) to own the design and evolution of our data warehouse. You will architect and build the medallion layers (bronze → silver → gold) on BigQuery, drive data modeling and design decisions, and partner directly with analytics, product, and game teams to turn ambiguous requirements into reliable, well-modeled datasets. You'll work as an AI-augmented engineer—modern AI tooling is part of how this team ships.\n\nThe core responsibilities for the job include the following:\n\nData Warehousing and Modeling: \nOwn the design of silver/gold layer tables: grain, join keys, schema, partitioning, and clustering decisions for large-scale datasets.\nBuild and maintain batch and streaming ETL pipelines (using big data technologies) feeding the medallion architecture.\nDesign idempotent, observable pipelines with QC gates, backfill strategies, and clear failure semantics.\nStandardize data models and conventions across multiple game products/projects.\nDeliver high-quality data pipelines with utmost importance to the correctness and availability of the data to different target audiences/systems.\n\nDesign Decisions and Technical Ownership: \nLead source-system profiling and drive grain, deduplication, and enrichment decisions backed by data, not assumptions.\nOwn trade-off calls: cost vs. freshness, load strategy, schema evolution, and query/slot optimization.\nDocument designs and decisions so they survive beyond the author.\n\nStakeholder Management: \nPartner with analysts, PMs, and game teams to translate business questions into warehouse requirements and reconcile instrumentation gaps.\nCommunicate design proposals and data profiling findings to both technical and leadership audiences.\n\nRequirements: \nExperience: 2+ years in data engineering, with deep data warehousing exposure at scale.\nSQL and BigQuery: Expert-level SQL; hands-on BigQuery optimization (partitioning, clustering, slots, cost).\nData Modeling: Strong command of medallion architecture, dimensional/fact modeling, grain definition, and schema design.\nProgramming: Production-grade Python; PySpark on Dataproc (or Databricks/EMR equivalents).\nStreaming: Working experience with Kafka-based ingestion (Confluent preferred).\nData Quality: Experience building validation, observability, and reconciliation into pipelines.\nStakeholder Skills: Proven ability to gather requirements, challenge assumptions, and communicate trade-offs clearly.\nEducation: Bachelor's/Master's in CS, engineering, or a related field (or equivalent experience).\n\nAI-Augmented Engineering (Must-Have): \nWe expect AI tooling to be part of your daily workflow, with sound judgment on when to trust and when to verify:\nHands-on daily use of AI coding agents: Claude Code / CLI, Claude web app, or equivalents (Cursor, Copilot, Gemini CLI).\nExperience creating reusable AI assets: custom skills/commands, prompt libraries, or repo-level agent context.\nFamiliarity with MCP or similar integrations connecting agents to warehouses and internal tools.\nAbility to demonstrate real examples of AI-accelerated work and where you drew the human-review line.\n\nPreferred: \nLLM-in-pipeline experience: AI-driven data quality checks, anomaly triage, or text-to-SQL for self-service analytics.\nBroader GCP: Pub/Sub, Cloud Run, Cloud Functions, Cloud SQL.\nGaming, fintech, or other high-volume consumer event domains.\nSkills\ndata, data engineer, Data Warehousing, Python, SQL","description_format":"text","description_chars":3444,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Game Development","Esports"],"lifecycle":[{"event":"open","at":"2026-10-26T14:35:00Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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