{"id":4209757,"url":"https://alion.io/job/i4dm-databricks-data-engineer-3","title":"Databricks Data Engineer","company":{"id":8388,"name":"I4Dm","domain":"i4dm.com","url":"https://alion.io/company/i4dm","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Comeet","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"CI/CD","optional":false},{"name":"Collibra","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scrum","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"ggplot2","optional":true},{"name":"Hadoop","optional":true},{"name":"Matplotlib","optional":true},{"name":"NLP","optional":true},{"name":"Plotly","optional":true},{"name":"R","optional":true},{"name":"Seaborn","optional":true}],"status":"live","first_seen_at":"2026-11-12T19:14:07Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-13T00:35:24Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Description\nAbout the Role:\nWe are seeking a hands-on Databricks Data Engineer to support ATLAS, the VA’s open-architected, multi-cloud enterprise data management and analytics platform. You will design, build, and operate scalable lakehouse solutions that ingest, condition, curate, and transform multi-source data into reliable, reusable enterprise data products. Using Databricks, Apache Spark, Delta Lake, Unity Catalog, structured streaming, and medallion architecture patterns, you will strengthen interoperability across ATLAS and its connected data platforms while ensuring consistent, secure access to governed data. You will collaborate with data scientists, analysts, product teams, and enterprise stakeholders to enable advanced analytics and machine-learning use cases that support evidence-based decisions and improve outcomes for Veterans and their families.\nKey Responsibilities\nDesign, develop, and maintain scalable batch and streaming data pipelines using Databricks, Apache Spark, PySpark, and Spark SQL.\nBuild and manage Delta Lake tables using the medallion (bronze/silver/gold) architecture to deliver reliable, analytics-ready data.\nDevelop real-time and near-real-time data ingestion solutions using Spark Structured Streaming and messaging platforms such as Kafka.\nConfigure and manage Databricks clusters, jobs, and workflows in production environments.\nImplement data governance, access controls, and security best practices using Unity Catalog.\nIntegrate data from a variety of source systems and destinations, supporting ETL/ELT and pipeline orchestration activities.\nOptimize existing data workflows and Spark jobs for performance, reliability, and cost efficiency.\nIntegrate Databricks development with CI/CD pipelines and enterprise SDLC tooling, including Git-based version control.\nCollaborate with data scientists and analysts to define data models and support machine learning and AI use cases, including model lifecycle management with MLflow.\nSupport advanced analytics use cases such as anomaly detection, risk scoring, and fraud analytics.\nMonitor and troubleshoot data processing jobs, implementing data quality checks and observability to ensure high availability.\nDocument data processes, frameworks, pipelines, and data mappings for technical and non-technical audiences.\nWork closely with scrum teams, product owners, and client stakeholders to deliver end-to-end data solutions.\nStay current on Databricks platform capabilities and industry trends to recommend best-fit tools and technologies.\nRequirements\nMinimum Qualifications\nBachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience).\n4+ years of experience in data engineering, analytics engineering, or big data development.\n2+ years of hands-on experience with the Databricks platform.\nProficiency in Apache Spark, PySpark, and Spark SQL.\nExperience with Databricks clusters, jobs/workflows, Delta Lake, and Unity Catalog in production environments.\nExperience with medallion architecture and Spark Structured Streaming.\nStrong Python and SQL skills for data engineering and data analysis.\nExperience with ETL/ELT processes and data pipeline orchestration.\nFamiliarity with cloud platforms such as AWS, Azure, or Google Cloud and their native data services.\nExperience integrating data solutions with CI/CD pipelines and Git-based version control workflows.\nUnderstanding of data governance, security, and access control best practices.\nExperience working in Agile development environments.\nAbility to obtain and maintain a Public Trust determination.\nExcellent analytical, problem-solving, and communication skills, with the ability to work with both technical and non-technical stakeholders.\nExperience with Synapse Pipelines, Azure Data Factory, Starburst, Azure Data Lake Service, Collibra for data cataloging, Azure ML/AI services, and Pyramid Analytics/Power BI for visualization.\nPreferred Qualifications\n Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional) or cloud platform certification.\nExperience implementing ML or AI solutions in Databricks, including MLflow-based model lifecycle management.\nKnowledge of machine learning, AI, or Natural Language Processing (NLP) techniques, including text mining.\nExperience supporting fraud analytics, risk scoring, or anomaly detection.\nExperience with distributed data and streaming tools such as Kafka, Hadoop, Hive, or Amazon EMR.\nExperience with data quality frameworks and observability/monitoring tooling.\nExperience with NoSQL databases.\nExperience with visualization packages such as Plotly, Seaborn, or ggplot2.\nExperience supporting federal government or regulated-industry programs, especially the Department of Veterans Affairs.","description_format":"text","description_chars":4799,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Custom Software Development"],"lifecycle":[{"event":"open","at":"2026-11-13T00:35:24Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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