{"id":2098075,"url":"https://alion.io/job/srm-technologies-senior-data-engineer","title":"Senior Data Engineer","company":{"id":59585,"name":"SRM Technologies","domain":"srmtech.com","url":"https://alion.io/company/srm-technologies","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","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":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_payroll_markers","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":90000,"max_usd":203000,"period":"year","method":"global_role_seniority_cell","sample_n":2127},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Agile","optional":true},{"name":"Amazon CloudWatch","optional":true},{"name":"AWS CDK","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"Dimensional Modeling","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"GitHub","optional":true},{"name":"GitLab","optional":true},{"name":"IAM","optional":true},{"name":"Scrum","optional":true}],"status":"live","first_seen_at":"2026-10-08T14:56:54Z","employer_posted_date":null,"last_verified_at":"2026-10-08T14:56:54Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"This is a remote position.Summary:\nRole:Senior Data Engineer\nExperience:8+ Years\nMandatory/Core:Python, PySpark, Snowflake, dbt, Apache Iceberg, AWS, SQL\nPreferred:AWS Glue, S3, EMR, Lambda, Airflow, Snowpipe/Snowpark, CI/CD, Terraform, Data Modeling\nRole Type:Senior hands-on Data Engineer\nFocus:Cloud Data Engineering, Lakehouse, Data Transformation, Performance Optimization and Production EngineeringDetailed information:\nSenior Data Engineer:\nExperience: 8+ years of overall Data Engineering experience, with strong hands-on experience building enterprise-scale cloud data platforms and pipelines.\n\nPrimary Skills:\n\nPython\n\nPySpark / Apache Spark\n\nSnowflake\n\ndbt (Data Build Tool)\n\nApache Iceberg\n\nAWS Data Services\n\nAdvancedSQL\n\nData Engineering / ETL / ELT\n\nData Lake / Lakehouse architecture\n\nSecondary / Preferred Skills:\n\nAWS services such as:\nS3\n\nAWS Glue\n\nEMR\n\nLambda\n\nStep Functions\n\nCloudWatch\n\nIAM\n\nApache Airflow or other workflow orchestration tools\n\nSnowflake performance optimization and cost optimization\n\nSnowpipe / Snowpark\n\nSpark performance tuning\n\nData modeling and dimensional modeling\n\nParquet and other columnar data formats\n\nData quality frameworks and automated validation\n\nCI/CD for data pipelines\n\nGit / GitHub / GitLab\n\nInfrastructure as Code such as Terraform or AWS CDK\n\nDocker / containerization\n\nData governance, lineage, security, and access control\n\nAgile/Scrum delivery experience\n\nJob Description:\nWe are looking for aSenior Data Engineerwith strong hands-on expertise inPython, PySpark, Snowflake, dbt, Apache Iceberg, and AWSto design, develop, and maintain scalable enterprise data solutions.\n\nThe candidate should have strong experience working with high-volume data processing, cloud-based data platforms, modern lakehouse architectures, ETL/ELT pipelines, data modeling, performance optimization, and production-grade engineering practices.\n\nThe ideal candidate should be capable of independently owning complex data-engineering components, contributing to technical design and architecture decisions, troubleshooting production issues, and providing technical guidance to other engineers.\n\nKey Responsibilities:\n1. Data Pipeline Engineering\n\nDesign, develop, test, and maintain scalableETL/ELT data pipelines.\n\nDevelop production-quality data-processing solutions usingPython and PySpark.\n\nBuild reusable frameworks and components for ingestion, transformation, validation, and publishing of data.\n\nProcess large structured, semi-structured, and distributed datasets.\n\nImplement incremental and batch-processing patterns where appropriate.\n\n2. Snowflake Development\n\nDesign and develop scalable data solutions usingSnowflake.\n\nDevelop complex SQL transformations, data models, views, and reusable data structures.\n\nOptimize Snowflake workloads for performance, scalability, and cost.\n\nImplement appropriate data-loading and transformation patterns between AWS data platforms and Snowflake.\n\nTroubleshoot performance and data-quality issues across Snowflake workloads.\n\n3. dbt Development\n\nBuild and maintain transformation pipelines usingdbt.\n\nDevelop modular, reusable, maintainable dbt models.\n\nImplement dbt tests and documentation.\n\nFollow appropriate development practices for source, staging, intermediate, and business-layer transformations.\n\nSupport automated deployment and CI/CD practices for dbt projects.\n\n4. Apache Iceberg / Lakehouse\n\nDesign and implement data-lake and lakehouse solutions usingApache Iceberg.\n\nBuild scalable table structures for large analytical datasets.\n\nWork with partitioning, schema evolution, incremental processing, and table-maintenance strategies.\n\nIntegrate Iceberg-based datasets with Spark and AWS-based data-processing services.\n\nEnsure efficient storage and query patterns for high-volume datasets.\n\n5. AWS Data Engineering\n\nDesign and implement cloud-native data solutions onAWS.\n\nBuild data-processing workloads leveraging services such asS3, Glue, EMR and Lambdawhere appropriate.\n\nImplement secure access patterns using AWS IAM.\n\nMonitor data workloads and troubleshoot operational issues.\n\nParticipate in designing scalable, reliable, secure, and cost-efficient cloud data architectures.\n\n6. Performance & Scalability\n\nDiagnose and optimizeSpark/PySpark jobs, SQL queries, Snowflake workloads, and data pipelines.\n\nIdentify bottlenecks involving compute, storage, partitioning, data skew, transformations, and queries.\n\nDesign solutions capable of supporting increasing data volumes without unnecessary infrastructure cost.\n\n7. Data Quality & Governance\n\nImplement automated data-quality checks across ingestion and transformation layers.\n\nEstablish proper logging, monitoring, exception handling, and reconciliation mechanisms.\n\nFollow organizational standards for data security, governance, lineage, and access controls.\n\nEnsure production pipelines are reliable, auditable, and maintainable.\n\n8. Engineering Best Practices\n\nWrite clean, modular, reusable, testable, and maintainable code.\n\nPerform code reviews and enforce engineering standards.\n\nImplement unit, integration, and data-validation testing.\n\nUse Git-based version control and CI/CD practices.\n\nCreate and maintain appropriate technical documentation.\n\n9. Senior-Level Responsibilities\n\nIndependently drive technically complex data-engineering requirements from design through production deployment.\n\nParticipate in solution design and architecture discussions.\n\nEvaluate alternative implementation approaches and recommend appropriate solutions.\n\nTroubleshoot complex production and performance issues.\n\nMentor junior and mid-level data engineers.\n\nCollaborate with Architects, Product Owners, Business Analysts, Data Scientists, QA, DevOps, and application teams.\n\nTranslate business/data requirements into scalable technical solutions.\n\nIdentify technical risks and proactively recommend improvements.\n\nCore Skills Expected\nA strong candidate should demonstratedeep hands-on capability, not merely theoretical exposure, in the following areas:\n\nArea\n\nExpected Capability\n\nPython\nAdvanced, production-quality data engineering development\n\nPySpark\nLarge-scale distributed processing, optimization and troubleshooting\n\nSnowflake\nDevelopment, modeling, optimization and performance tuning\n\ndbt\nModels, tests, macros, documentation and deployment practices\n\nApache Iceberg\nLakehouse/table design, partitioning, schema evolution and optimization\n\nAWS\nHands-on cloud data platform development\nSQL\nAdvanced SQL, query optimization and analytical processing\n\nData Engineering\nETL/ELT, batch/incremental pipelines, data quality and orchestration\n\nData Architecture\nData Lake, Data Warehouse and Lakehouse concepts\n\nEngineering Practices\nGit, testing, code reviews, CI/CD and production support\n\nPreferred Qualifications\n\nBachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.\n\nStrong experience deliveringenterprise-scale cloud data platforms.\n\nExperience migrating legacy data workloads to modern AWS/Snowflake architectures.\n\nExperience working with very large datasets and distributed processing.\n\nKnowledge of data security and governance practices.\n\nExperience working in Agile delivery environments.\n\nAWS and/or Snowflake certification is an added advantage.\n\nOriginally posted on Himalayas","description_format":"text","description_chars":7313,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Embedded Software & RTOS","Custom Software Development","Engineering Services"],"lifecycle":[{"event":"open","at":"2026-10-08T17:28:00Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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