{"id":4167848,"url":"https://alion.io/job/synechron-data-engineer-python-pyspark-sql-etl","title":"Data Engineer – Python, PySpark, SQL & ETL","company":{"id":5306,"name":"Synechron","domain":"synechron.com","url":"https://alion.io/company/synechron","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":94,"open_postings":27,"ghost_share":0.074,"stale_share":0.148,"repost_share":0.222,"time_to_fill_p50_days":21,"computed_at":"2026-11-12T10:38:37Z"}},"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":["Bengaluru, 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":"Agile","optional":false},{"name":"CI/CD","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Hadoop","optional":false},{"name":"Jupyter Notebook","optional":false},{"name":"Least Privilege","optional":false},{"name":"Oracle","optional":false},{"name":"Pandas","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-12T13:55:08Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T13:55:08Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Job Summary\nSynechron is seeking a Data Engineer with 5+ years of experience in building data marts, ETL pipelines, and large-scale data-processing solutions.\nThe role will focus on designing, developing, optimizing, and supporting scalable data pipelines using Python, PySpark, Spark, SQL, and data-warehousing technologies. The position will cover the end-to-end software development lifecycle, including development, testing, UAT support, production deployment, issue resolution, and post-production support.\nThe role will contribute to business objectives by delivering reliable and efficient data solutions, improving data quality and accessibility, supporting informed decision-making, and enabling consistent data operations across enterprise environments.\nSoftware Requirements\nRequired Software Skills\nPython: Strong hands-on experience developing data-processing applications, ETL workflows, automation scripts, and production services.\nPySpark and Spark: Strong experience building distributed data-processing and ETL solutions.\nPandas: Experience with data preparation, analysis, transformation, validation, and profiling.\nSQL: Strong experience with data extraction, transformation, validation, analysis, and query optimization.\nOracle SQL: Strong knowledge of Oracle SQL, complex queries, joins, aggregations, subqueries, stored procedures, and performance optimization.\nETL development: Experience designing, developing, testing, deploying, and supporting ETL and data-pipeline solutions.\nData warehousing: Strong understanding of data-warehouse concepts, dimensional modelling, data marts, transformations, fact tables, dimensions, and data quality.\nHadoop, Hive, and MapReduce: Experience working with distributed data-processing and big-data technologies.\nSQL and NoSQL databases: Experience working with structured, semi-structured, and unstructured data.\nJupyter Notebook: Experience using notebooks for data analysis, development, validation, troubleshooting, and technical exploration.\nCI/CD pipelines: Familiarity with automated build, testing, deployment, and release processes.\nTesting and data validation: Experience validating data transformations, pipeline results, data quality, and application behavior.\nSoftware engineering practices: Understanding of coding standards, version control, documentation, code reviews, testing, and maintainable development practices.\nPreferred Software Skills\nExperience with banking or financial-services data and reporting requirements.\nExposure to cloud-based data platforms and data-processing services.\nFamiliarity with workflow scheduling, orchestration, monitoring, and alerting tools.\nExperience with automated data-quality checks, reconciliation, lineage, and metadata management.\nExposure to infrastructure automation, containerization, and modern DevOps practices.\nFamiliarity with tools supporting data pipeline observability and performance analysis.\nVersion and Experience Guidance\nSpecific software versions are not mandated in the requirements.\nCandidates should have experience with current, production-supported versions of Python, PySpark, Spark, Pandas, Oracle SQL, Hadoop, Hive, MapReduce, and related tools.\nCandidates should be able to assess compatibility, manage dependencies, support upgrades, and follow version-control practices.\nExperience should include production data engineering, pipeline support, and end-to-end SDLC participation rather than only academic or prototype development.\nOverall Responsibilities\nDesign, develop, maintain, and optimize scalable ETL and data-pipeline solutions using Python and PySpark.\nBuild and enhance data marts and data-transformation processes.\nDevelop distributed data-processing solutions using Spark, Hadoop, Hive, and MapReduce.\nWork with structured, semi-structured, and unstructured data from multiple sources.\nDevelop and optimize Oracle SQL queries for data extraction, transformation, analysis, and validation.\nAnalyze and debug data issues using Oracle SQL, PySpark, Pandas, and structured data-analysis techniques.\nPerform end-to-end SDLC activities, including development, testing, UAT support, bug fixing, production deployment, and post-production support.\nCollaborate with cross-functional teams to define data requirements, validate solutions, resolve defects, and improve data quality.\nImplement data validation, reconciliation, completeness, accuracy, and consistency checks.\nSupport CI/CD implementation for data pipelines, applications, and deployment workflows.\nMonitor production environments and troubleshoot pipeline failures, data-quality issues, performance problems, and processing delays.\nCreate and maintain technical documentation, data mappings, transformation logic, support procedures, and operational runbooks.\nApply software engineering best practices, coding standards, testing practices, and maintainable design principles.\nContribute to performance improvements through query optimization, efficient transformations, appropriate partitioning, resource management, and pipeline tuning.\nIdentify opportunities to improve automation, reliability, data quality, maintainability, and sustainable use of processing resources.\nContribute to measurable outcomes, including reliable data delivery, reduced processing time, improved data accuracy, fewer production incidents, and successful release execution.\nTechnical Skills (By Category)\nProgramming Languages\nEssential\nStrong Python development experience.\nStrong PySpark and Spark programming experience.\nExperience using Pandas for data analysis, transformation, profiling, and validation.\nAbility to write modular, maintainable, testable, and production-ready code.\nAbility to develop reusable data-processing components, ETL logic, validation utilities, and automation scripts.\nPreferred\nExperience with additional programming or scripting languages used in data engineering and distributed processing.\nFamiliarity with asynchronous or event-driven data-processing patterns.\nExperience developing reusable libraries and shared data-engineering components.\nDatabases/Data Management\nEssential\nStrong Oracle SQL experience.\nExperience with SQL and NoSQL databases.\nUnderstanding of data-warehouse concepts and data-mart design.\nExperience working with structured, semi-structured, and unstructured data.\nUnderstanding of data modelling, dimensional modelling, schemas, fact tables, dimensions, keys, joins, aggregations, and transformations.\nExperience with data quality, validation, reconciliation, completeness, accuracy, and consistency checks.\nAbility to optimize queries and investigate data-related application or pipeline issues.\nUnderstanding of data lineage, source-to-target mapping, metadata, data retention, and controlled data access.\nPreferred\nExperience with banking or financial-services data structures and reporting requirements.\nFamiliarity with data lakes, analytical data stores, and distributed data architectures.\nExperience with partitioning, indexing, compression, schema evolution, and high-volume data processing.\nExposure to automated data-quality frameworks, data reconciliation, and metadata management.\nCloud Technologies\nEssential\nUnderstanding of scalable data-processing and data-pipeline deployment environments.\nAbility to support development, testing, UAT, staging, and production data environments.\nUnderstanding of resource management, performance, availability, monitoring, and operational support for data platforms.\nPreferred\nExperience with cloud-based data platforms and distributed processing services.\nFamiliarity with cloud storage, managed data-processing services, data warehouses, and orchestration tools.\nExposure to cloud-native CI/CD, infrastructure automation, and containerized data workloads.\nUnderstanding of processing optimization, resource utilization, and sustainable data-engineering practices.\nFrameworks and Libraries\nEssential\nPython.\nPySpark and Spark.\nPandas.\nOracle SQL.\nHadoop.\nHive.\nMapReduce.\nSQL and NoSQL database technologies.\nJupyter Notebook.\nETL and data-pipeline development frameworks.\nCI/CD tools and automated deployment workflows.\nData validation, testing, monitoring, and troubleshooting utilities.\nPreferred\nWorkflow orchestration and scheduling frameworks.\nData-quality, reconciliation, lineage, and metadata-management tools.\nPipeline observability, logging, metrics, and alerting frameworks.\nLibraries supporting performance profiling, distributed processing, and automated testing.\nTools supporting cloud-based data integration and analytics.\nDevelopment Tools and Methodologies\nEssential\nExperience across the data-engineering lifecycle, including requirements analysis, design, development, testing, UAT support, deployment, monitoring, troubleshooting, and production support.\nExperience with ETL development and data-mart implementation.\nFamiliarity with version control, code reviews, coding standards, technical documentation, and collaborative development practices.\nExperience implementing data validation, unit testing, integration testing, regression testing, and pipeline testing.\nExperience supporting CI/CD implementation for data applications and deployment workflows.\nAbility to create data mappings, transformation specifications, test plans, support procedures, and operational runbooks.\nFamiliarity with Agile or iterative delivery practices.\nExperience performing root-cause analysis and resolving production data-pipeline issues.\nPreferred\nExperience with automated data-pipeline deployment and release management.\nFamiliarity with infrastructure as code, containers, and DevOps practices.\nExperience with pipeline monitoring, operational dashboards, alerting, and service-level reporting.\nExposure to automated rollback, recovery, retry, and failure-handling patterns.\nExperience supporting continuous improvement and process standardization.\nSecurity Protocols\nEssential\nUnderstanding of secure data access, authentication, authorization, and least-privilege principles.\nAbility to protect sensitive business, customer, financial, and operational data.\nUnderstanding of secure database connections, credential management, data masking, encryption, and controlled data handling.\nAbility to follow security, privacy, compliance, change-management, and access-control requirements.\nAwareness of risks related to unauthorized access, data leakage, insecure scripts, exposed credentials, and uncontrolled data movement.\nAbility to maintain auditability and traceability across data transformations, deployments, and production changes.\nPreferred\nExperience supporting data-security reviews, audit activities, access reviews, and compliance controls.\nFamiliarity with secrets management, encryption in transit and at rest, and security monitoring.\nExperience integrating security and quality checks into CI/CD pipelines.\nKnowledge of security requirements for banking and financial-services data.\nExperience Requirements\nOverall experience: Minimum 5 years of professional experience in data engineering, ETL development, data warehousing, big-data engineering, or a related technical role.\nData-engineering experience: Strong hands-on experience building data marts, ETL pipelines, data transformations, and large-scale data-processing solutions.\nTechnical experience: Strong experience with Python, PySpark, Spark, Pandas, SQL, Oracle SQL, Hadoop, Hive, MapReduce, and SQL and NoSQL databases.\nData-warehouse experience: Experience with data-warehouse concepts, data modelling, data marts, source-to-target mapping, data transformations, and data-quality controls.\nSDLC experience: Experience across development, UAT support, bug fixing, production deployment, monitoring, and post-production support.\nProduction experience: Experience troubleshooting data-pipeline failures, query issues, data-quality defects, performance problems, and production incidents.\nDomain preference: Banking and financial-services experience is highly preferred.\nIndustry preference: Experience 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