{"id":3598685,"url":"https://alion.io/job/geektrust-data-engineer-etlpyspark","title":"Data Engineer - ETL/PySpark","company":{"id":702244,"name":"Geektrust","domain":"geektrust.com","url":"https://alion.io/company/geektrust-2","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":"junior","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":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Azure","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Databricks","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-02T06:31:17Z","employer_posted_date":null,"last_verified_at":"2026-11-02T06:31:17Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Role overview :\n\n- 2 - 6 years of experience in data engineering or analytics engineering roles.\n\n- Strong problem-solving and ability to work independently with guidance from senior engineers.\n\n- Good communication skills to translate business requirements into technical data solutions.\n\n- Ability to work in Agile environments and collaborate with global teams across time zones.\n\n- Hands-on experience with Azure Databricks and Apache Spark (PySpark or Scala) for large-scale data processing.\n\n- Strong SQL skills, including complex queries, window functions, CTEs, and query optimization for both transactional and analytical workloads.\n\n- Experience with SQL Server: data warehousing concepts, schema design, indexing, and optimization for analytics.\n\n- Familiarity with Azure data services: Data Lake Storage, Synapse Analytics, Data Factory, or Blob Storage.\n\n- Understanding of ETL/ELT concepts, data modeling, dimensional modeling (fact/dimension tables), and slowly changing dimensions.\n\n- Experience with batch and/or stream processing concepts; familiarity with event-driven architectures.\n\n- Knowledge of data quality frameworks, testing strategies, and monitoring/alerting for data pipelines.\n\n- Basic understanding of relational and NoSQL databases (Cosmos DB exposure is beneficial).\n\nWhat would you do here :\n\n- The Data Engineering team builds and operates robust, scalable data platforms on Azure and Databricks, closely partnering with domain PODs, Risk, Finance, and Analytics teams across the globe. \n\n- You'll work on end-to-end data pipelines that ingest, transform, and serve data to multiple downstream consumers with strict quality and SLA requirements.\n\n- Build and maintain ETL/ELT pipelines on Azure Databricks using PySpark or Scala to ingest, transform, and deliver data products at scale.\n\n- Ingest data from SQL Server, Cosmos DB, event streams, APIs, and external sources into cloud data lakes and Databricks lakehouses.\n\n- Design and implement data quality checks, validation rules, and monitoring to ensure reliability and early detection of anomalies.\n\n- Optimize Spark jobs for performance and cost, including cluster sizing, partitioning strategies, and caching mechanisms.\n\n- Document data pipelines, lineage, and schemas; 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