{"id":3273638,"url":"https://alion.io/job/schneider-electric-senior-data-engineer-aws-cloud-data-platform","title":"Senior Data Engineer – AWS Cloud Data Platform","company":{"id":1266,"name":"Schneider Electric","domain":"se.com","url":"https://alion.io/company/schneider-electric","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":{"grade":"B","score":75,"open_postings":387,"ghost_share":0,"stale_share":0.99,"repost_share":0,"time_to_fill_p50_days":31,"computed_at":"2026-10-28T05:59:10Z"}},"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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":24000,"max_usd":52000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache HTTP Server","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"AWS","optional":false},{"name":"AWS Glue","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CAPA","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Data Vault","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Informatica","optional":false},{"name":"Least Privilege","optional":false},{"name":"Oracle","optional":false},{"name":"PostgreSQL","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":"Apache Kafka","optional":true},{"name":"Databricks","optional":true},{"name":"dbt","optional":true},{"name":"Kubernetes","optional":true},{"name":"Master Data Management","optional":true}],"status":"live","first_seen_at":"2026-10-26T13:01:52Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-28T08:02:50Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"We are seeking a highly skilled, hands-on Senior Data Engineer to architect, build, modernise, and manage enterprise-scale data platforms on AWS.\nThe ideal candidate must have extensive experience in cloud data engineering, Data Lakehouse architecture, enterprise data warehousing, cloud migration, data modelling, distributed data processing, and performance optimisation. This role requires an engineer who can independently translate complex business requirements into secure, scalable, resilient, and cost-effective data solutions.\nThe candidate will be responsible for establishing engineering standards, designing reusable data frameworks, implementing governance and data quality controls, and enabling analytics, reporting, AI, and business intelligence use cases across the organisation.\nThis is not a purely development or support-oriented position. The successful candidate is expected to demonstrate strong architectural thinking, advanced hands-on engineering capabilities, production troubleshooting skills, and the ability to provide technical leadership across complex enterprise data initiatives.\nExperience with Informatica PowerCenter or Informatica Intelligent Cloud Services (IICS) is a strong advantage but is not mandatory.\nKey Responsibilities\nEnterprise Data Architecture and Strategy\nDesign and implement enterprise-scale data architectures aligned with business, analytics, and technology objectives.\nArchitect scalable Data Lake, Data Lakehouse, and Enterprise Data Warehouse solutions on AWS.\nDefine architecture principles, engineering standards, reusable patterns, and development best practices.\nDesign multi-layer data architectures such as Raw, Standardised, Curated, and Consumption layers.\nEvaluate technical requirements and select appropriate AWS services, data formats, processing engines, and orchestration patterns.\nTranslate complex business requirements into scalable, maintainable, and production-ready technical solutions.\nParticipate in architecture reviews and provide technical direction for data platform initiatives.\nEvaluate emerging data technologies and recommend platform improvements based on business value, scalability, security, and cost.\nAWS Data Platform Design and Engineering\nArchitect and develop end-to-end data solutions using AWS-native services.\nBuild scalable frameworks for batch, near-real-time, event-driven, and streaming data processing.\nDesign secure, highly available, fault-tolerant, and cost-optimised cloud data platforms.\nBuild metadata-driven and configuration-driven ingestion and transformation frameworks.\nDesign solutions for loading data from databases, APIs, files, message queues, SaaS applications, and enterprise platforms.\nImplement orchestration, dependency management, retries, restartability, error handling, and operational recovery capabilities.\nEstablish monitoring, alerting, logging, auditability, and observability standards for production pipelines.\nOptimise storage, processing, and query performance across AWS data services.\nData Lakehouse and Data Warehouse Engineering\nDesign and implement modern Data Lakehouse architectures using technologies such as:\nApache Iceberg\nDelta Lake\nApache Parquet\nDefine appropriate partitioning, compaction, data retention, schema evolution, and file optimisation strategies.\nDesign enterprise and dimensional data models using:\nFact and dimension modelling\nStar and snowflake schemas\nSlowly Changing Dimensions\nData Vault modelling\nCreate scalable ETL and ELT frameworks supporting enterprise reporting, analytics, self-service BI, data science, and AI workloads.\nDesign data models at the correct business grain and establish reliable relationships across multiple source systems.\nImplement history management, incremental processing, change data capture, late-arriving data handling, reconciliation, and audit controls.\nEnable both batch and near-real-time data consumption while maintaining data consistency and traceability.\nAdvanced Data Engineering\nDevelop complex production-grade data pipelines using Python, PySpark, SQL, AWS Glue, and related AWS services.\nProcess high-volume structured, semi-structured, and unstructured datasets.\nDesign efficient transformation logic capable of handling large-scale distributed workloads.\nDevelop reusable components for ingestion, transformation, validation, reconciliation, exception handling, and operational monitoring.\nReview code and ensure compliance with engineering, security, performance, and maintainability standards.\nImplement unit testing, integration testing, regression testing, and automated data validation.\nDiagnose and resolve complex data processing, performance, stability, and production issues.\nPySpark and Distributed Processing\nDevelop and optimise complex PySpark workloads for large-scale data processing.\nAnalyse Spark execution plans and identify performance bottlenecks.\nApply appropriate optimisation techniques, including:\nPartitioning and repartitioning\nBroadcast joins\nJoin strategy optimisation\nData-skew handling\nPredicate pushdown\nPartition pruning\nCaching and persistence\nMemory and executor optimisation\nSmall-file management\nTroubleshoot failed or slow-running Spark jobs and implement sustainable fixes.\nSelect suitable processing patterns based on data size, workload behaviour, latency, and cost requirements.\nAmazon Redshift Engineering and Optimisation\nDesign and maintain scalable analytical data models in Amazon Redshift.\nDemonstrate advanced knowledge of:\nDistribution styles and distribution keys\nSort keys\nWorkload Management\nQuery Monitoring Rules\nMaterialised views\nRedshift Spectrum\nData sharing\nQuery execution plans\nTable maintenance and statistics\nOptimise SQL queries and data models for performance, concurrency, and workload efficiency.\nDiagnose query failures caused by data skew, excessive joins, intermediate row growth, memory pressure, or disk spilling.\nDesign appropriate loading, incremental processing, deduplication, and reconciliation patterns.\nMonitor Redshift workload behaviour and recommend improvements to queries, physical design, and processing patterns.\nEnterprise Data Integration\nDesign and oversee integrations across enterprise platforms, including:\nERP systems\nCRM systems\nSupply chain and planning applications\nLogistics and manufacturing systems\nFinance platforms\neCommerce applications\nThird-party and SaaS solutions\nDesign cross-system mapping and reference-data solutions where different platforms use different identifiers.\nDefine data contracts, interface specifications, schema standards, and integration controls.\nEstablish mechanisms for duplicate detection, referential validation, lineage tracking, and source-to-target reconciliation.\nWork with business and technical stakeholders to validate data grain, business rules, transformation logic, and expected outcomes.\nCloud Data Migration\nLead migration of legacy and on-premises data platforms to AWS.\nAssess existing databases, ETL workflows, dependencies, data volumes, and operational requirements.\nDefine migration strategies covering re-platforming, refactoring, parallel runs, validation, cutover, and rollback.\nRe-engineer legacy pipelines into scalable cloud-native solutions where appropriate.\nImplement reconciliation and validation controls to confirm completeness and accuracy between source and target platforms.\nIdentify migration risks, technical constraints, and performance issues and recommend mitigation measures.\nData Governance, Quality, and Security\nImplement engineering controls for data governance, metadata management, lineage, cataloguing, and data quality.\nEstablish automated data quality validations covering:\nCompleteness\nUniqueness\nAccuracy\nConsistency\nReferential integrity\nTimeliness\nDesign audit frameworks to track source-to-target movement, processing status, exceptions, and reconciliation results.\nImplement role-based access controls, encryption, secrets management, and least-privilege access.\nApply data protection controls for sensitive, confidential, and personally identifiable information.\nPartner with data governance, architecture, infrastructure, and cybersecurity teams to maintain organisational standards.\nSupport data ownership, stewardship, glossary, metadata, and lineage initiatives.\nDevOps, Automation, and Infrastructure as Code\nImplement CI/CD practices for data pipelines and cloud infrastructure.\nAutomate build, testing, deployment, configuration, and rollback activities.\nUse infrastructure-as-code tools such as AWS CloudFormation or Terraform.\nImplement source control, branching, peer review, and release management standards.\nSeparate code, configuration, credentials, and environment-specific parameters.\nBuild deployment processes that support development, test, staging, and production environments.\nImprove engineering productivity through reusable templates, libraries, and automation.\nProduction Support and Operational Excellence\nTake ownership of complex production incidents and drive them through resolution.\nPerform root-cause analysis and implement permanent corrective and preventive actions.\nDefine operational metrics, pipeline service-level expectations, recovery procedures, and support documentation.\nImprove platform reliability through monitoring, proactive alerting, automation, and failure recovery.\nSupport production releases and ensure appropriate technical validation before deployment.\nIdentify recurring operational issues and eliminate them through engineering improvements.\nTechnical Leadership and Collaboration\nProvide technical leadership and guidance to data engineers.\nMentor engineers on AWS, SQL, PySpark, data modelling, testing, and engineering best practices.\nLead solution design discussions, architecture reviews, code reviews, and technical problem-solving sessions.\nWork closely with business stakeholders, architects, analysts, product owners, BI teams, and data governance teams.\nChallenge incomplete or inefficient designs and recommend scalable alternatives.\nCommunicate technical risks, dependencies, trade-offs, and recommendations clearly to both technical and non-technical stakeholders.\nIndependently own technical delivery from requirement analysis through production implementation.\n Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related discipline.\n8-10 years of experience in data engineering, data warehousing, data integration, or enterprise data platforms.\nAt least 5 years of hands-on experience designing and implementing AWS-based data solutions.\nProven experience delivering enterprise-scale data platforms and complex data engineering programmes.\nDemonstrated ability to independently own solution design, development, deployment, performance optimisation, and production support.\nStrong experience working with large-volume and complex datasets from multiple source systems.\nStrong understanding of cloud-native architecture, distributed processing, data security, and operational resilience.\nExperience providing technical direction, conducting design reviews, and mentoring other engineers.\nMandatory Technical Skills\nAWS Data Services\nThe candidate must have strong hands-on experience with most of the following:\nAmazon S3\nAWS Glue\nAmazon Redshift\nAmazon Athena\nAmazon EMR\nAWS Lambda\nAWS Step Functions\nAmazon MWAA / Apache Airflow\nAWS Lake Formation\nAmazon EventBridge\nAmazon SQS\nAmazon SNS\nAWS Identity and Access Management\nAWS Secrets Manager\nAmazon CloudWatch\nAWS CloudFormation or Terraform\nProgramming and Data Processing\nAdvanced SQL\nPython\nPySpark\nApache Spark\nShell scripting\nDistributed data processing\nPerformance tuning and troubleshooting\nData structure and algorithm fundamentals relevant to data processing\nData Architecture and Modelling\nData Lakehouse architecture\nEnterprise Data Warehouse design\nDimensional modelling\nData Vault modelling\nFact and dimension design\nStar and snowflake schemas\nSlowly Changing Dimensions\nData partitioning and lifecycle management\nMet...","description_format":"text","description_chars":16550,"description_truncated":true,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Industrial Automation","Energy Management","Electrical Equipment & 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