{"id":3316326,"url":"https://alion.io/job/random-trees-data-engineer","title":"Data Engineer","company":{"id":3801016,"name":"Random Trees","domain":"randomtrees.com","url":"https://alion.io/company/random-trees","size_band":"201-500","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":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":41000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Bitbucket","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"IAM","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true}],"status":"live","first_seen_at":"2026-10-26T23:50:14Z","employer_posted_date":null,"last_verified_at":"2026-10-26T23:50:14Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"We are seeking an experienced Data Engineer with 5+ years of hands-on expertise across the modern data stack to design, build, and maintain high-performance ELT pipelines. In this role, you will be the core technical owner of our analytics foundation—leveraging Snowflake, dbt, hosted on cloud infrastructure (AWS, Azure, or GCP). You will partner closely with data analysts, analytics engineers, and business stakeholders to ensure our data models are scalable, reliable, and governed.\nKey Responsibilities\nData Pipeline Architecture & ELT: Design, develop, and optimize robust, idempotent ELT data pipelines moving data from diverse operational sources into Snowflake.\nTransformation & Modeling (dbt): Architect modular, production-grade dbt projects. Implement dimension and fact tables (Kimball/medallion architecture), write reusable Jinja macros, and establish automated testing (data freshness, uniqueness, referential integrity).\nWorkflow Orchestration (Airflow): Build, monitor, and maintain production Airflow DAGs with proper error handling, SLA alerts, retries, and dependency management.\nSnowflak & Optimization: Configure and optimize virtual warehouses, storage, clustering keys, zero-copy cloning, and Time Travel. Enforce cost-governance practices and resource monitors.\nCloud Infrastructure & Ingestion: Integrate cloud storage services (e.g., AWS S3, Azure Blob/ADLS, or Google Cloud Storage) with Snowflake using Snowpipe, external stages, and event-driven architectures.\nData Quality & Governance: Establish schema migrations, data testing frameworks, and role-based access control (RBAC), object tagging, and data masking policies.\nCI/CD & DataOps: Implement version control best practices, automated deployment pipelines (GitHub Actions, GitLab CI, or Bitbucket Pipelines), and environment management for data assets.\nQualifications & Requirements\nMust-Have:\nExperience: 5+ years of dedicated professional experience in data engineering, data warehousing, or business intelligence backend systems.\nSnowflake: Deep expertise in Snowflake architecture, query profiling, warehouse right-sizing, Snowpipe, and security implementations (RBAC, row-level security).\ndbt (Data Build Tool): Proven track record modeling complex datasets in dbt Core or dbt Cloud, using incremental models, snapshots, tests, documentation, and Jinja/SQL macros.\nAny Major Cloud Platform: Solid working experience with at least one major cloud provider (AWS, Azure, or GCP), particularly with IAM roles, object storage, and serverless compute.\nProgramming & SQL: Mastery of advanced SQL (window functions, CTEs, query plan optimization) and intermediate-to-advanced proficiency in Python for data processing and automation.\nVersion Control & CI/CD: Hands-on experience with Git branching workflows and automated testing/deployment pipelines.\nNice-to-Have:\nExperience with streaming technologies (Apache Kafka, AWS Kinesis) or Snowflake Streaming Snowpipe.\nExposure to Snowflake Dynamic Tables, Streams & Tasks, or Snowpark.\nRelevant certifications: SnowPro Core / Advanced, dbt Certified Developer, or Associate/Professional Cloud Architect/Data Engineer (AWS/Azure/GCP).\nFamiliarity with data observability tools 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