{"id":4028422,"url":"https://alion.io/job/cubic-corporation-senior-data-engineer","title":"Senior Data Engineer","company":{"id":20722,"name":"Cubic Corporation","domain":"cubic.com","url":"https://alion.io/company/cubic-corporation","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":82,"open_postings":17,"ghost_share":0,"stale_share":0.706,"repost_share":0,"time_to_fill_p50_days":27,"computed_at":"2026-11-10T06:52:35Z"}},"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":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure SQL Database","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Airflow","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Feature Store","optional":true},{"name":"LLM","optional":true},{"name":"Machine Learning","optional":true},{"name":"MLFlow","optional":true},{"name":"Power BI","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"ServiceNow","optional":true},{"name":"TensorFlow","optional":true},{"name":"XGBoost","optional":true}],"status":"live","first_seen_at":"2026-11-10T17:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-10T17:35:30Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"The Senior Data Engineer will play a key role in designing, building, and maintaining scalable data pipelines, integrations, and analytical platforms that support CTS's global analytics and reporting platforms. This role is responsible for developing high-quality ETL/ELT processes, managing cloud data environments, ensuring data quality, and enabling advanced analytics and reporting across major programs.\n\nYou will work extensively with Azure Data Factory, Azure Databricks, Python, SQL (Oracle/SQL Server), data modelling, data lakes, and enterprise data warehouse structures to support KPI engines, device analytics, predictive modelling, and operational performance insights.\n\nKey Responsibilities:\nDesign, build, and optimize data pipelines using Azure Data Factory and Databricks (PySpark/Spark SQL).\nDevelop robust ETL/ELT processes to ingest, transform, and validate large volumes of operational, telemetry, incident, and transactional data.\nImplement scalable workflows leveraging Azure services such as Data Lake Storage, Azure Synapse, Azure SQL Databases, Key Vault, Logic Apps, and Functions.\nBuild efficient advanced SQL queries and stored procedures across Azure Synapse and Azure SQL Server.\nCollaborate with data analysts, engineers, performance assurance, and operations teams to enable reliable, accurate datasets for reporting, KPIs, and predictive analytics.\nDesign and maintain data models, schemas, tables, and metadata following COE architecture patterns.\nImplement strong data quality, validation, and monitoring frameworks to ensure accuracy and reliability across global programs.\n\nRequired Skills and Qualifications:\nBachelor's/master's degree in Computer Science, Data Analytics, Engineering, Mathematics, or related field.\n5+ years of experience in data engineering roles.\nStrong expertise with Azure Data Factory (ADF) pipelines, Azure Databricks, mapping data flows, and orchestrations.\nAdvanced experience with Azure Databricks (PySpark, Spark SQL, Delta Lake, notebooks, clusters).\nHigh proficiency in Python for ETL, automation, and data transformation.\nStrong SQL skills across Azure Synapse and Azure SQL, including query tuning and complex transformations.\nSolid understanding of data modelling, warehousing, star/snowflake schema, Medallion architecture, and distributed data processing.\nExperience handling large-scale datasets and complex data domains.\nFamiliarity with CI/CD practices, Git branching, and DevOps pipelines.\nExperience with data governance, data cataloging, and managing structured/unstructured data in cloud environments.\n\nPreferred Skills:\nExperience with Azure Data Factory (ADF) pipelines, Azure Databricks, Databricks Workflows, or Apache Airflow.\nExposure to real-time/streaming data technologies (Event Hub, Kafka, Spark Streaming).\nExposure to machine learning algorithms, feature engineering, model evaluation, hyperparameter tuning, LLM fundamentals, and scikit-learn/XGBoost. Knowledge of deep learning, PyTorch/TensorFlow, MLflow, model deployment, and MLOps practices.\nExperience integrating APIs, flat files, and operational systems such as ServiceNow.\nExperience with machine learning pipelines, feature stores, or model operationalization.\nFamiliarity with Power BI dataset design and optimization.\nUnderstanding of multi-region cloud environments and enterprise architecture patterns.\n\nSoft Skills:\nStrong analytical mindset with exceptional problem-solving abilities.\nAbility to work independently and take ownership of deliverables.\nExcellent communication skills, capable of simplifying complex concepts for non-technical stakeholders.\nComfortable working in a fast-paced, global, multi-time zone team.\nHigh attention to detail, data quality, and accuracy.\nStrong sense of accountability, adaptability, and continuous improvement.\nAbility to engage effectively with engineering, operations, and business stakeholders.\nProactive, resourceful, and committed to delivering high-quality outcomes.\n\nRole Impact & Success Measures:\nThe Senior Data Engineer will design, build, and maintain enterprise data warehouse solutions for analytics and reporting, develop and optimize ETL/ELT pipelines to load and transform data from multiple sources. Implement dimensional modelling (star/snowflake schemas) for performance and scalability, and ensure data quality, governance, and performance tuning across the warehouse whilst strengthening solutions using Lakehouse, Data Warehouse, and Real-Time Analytics.\n\nSuccess in the First 3-6 Months:\nBuild a solid understanding of CTS datasets, KPI frameworks, and key operational systems.\nGain strong understanding of CTS datasets, pipelines, and Azure architecture.\nDeliver reliable ADF and Databricks pipelines for ingestion and transformation.\nImprove data quality and performance of existing workflows.\nSupport analysts and operations teams with accurate, well-structured datasets.\n\nSuccess in 6-12 Months:\nTake ownership of end-to-end analytical workstreams with minimal supervision.\nOwn end-to-end pipeline development with minimal supervision.\nImplement scalable, reusable engineering patterns and metadata-driven frameworks.\nImprove efficiency through automation, orchestration, and optimization.\nIntegrate new data sources to support KPIs, device analytics, and predictive modelling.\n\nLong-Term Success (12+ Months):\nContribute to CTS Analytics strategy, standards, and global operating model improvements.\nContribute to global COE standards for data engineering and cloud architecture.\nBecome SME for key pipelines, data domains, or Azure components.\nLead continuous improvement initiatives around quality, governance, and automation.\nSupport junior engineers and uplift engineering capability within the COE.\nSkills\nADF, Azure Data Factory, Azure SQL, Azure Synapse, Databricks, Delta Lake, PySpark, Python, SQL, Spark 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