Enterprise Data Engineer - Cloud Data Pipelines, GCP, Big Data, and Data Governance

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Job Summary Synechron is seeking an experienced Data Engineer supporting cloud-based big data solutions to design, build, and maintain scalable data pipelines and analytics infrastructure. This role focuses on leveraging Google Cloud Platform (GCP), Spark, Hive, and Python to deliver reliable, high-performance data workflows that support enterprise analytics, data migration, and operational excellence. The candidate will collaborate with cross-functional stakeholders to optimize data processing, ensure data quality, and support enterprise data strategies aligned with business objectives. Software Requirements Required Software Proficiency: GCP services: BigQuery, Cloud Storage, Dataflow, or equivalent (latest version) - extensive experience supporting scalable data processing in cloud environments (supporting 6-8 years) Apache Spark - strong hands-on experience supporting large-scale data processing and big data workflows (supporting 6+ years) Hive - experience designing and managing data schemas and querying large datasets supporting data warehousing needs (supporting 5+ years) Python - proficiency supporting data pipeline automation, scripting, and transformation (supporting 4+ years) SQL (supporting databases like SQL Server, Oracle, PostgreSQL) - supporting data validation, migration, and operational reporting Preferred Software Skills: Cloud automation tools: Terraform, Dataflow, or Cloud Datafusion supporting environment automation (preferred) Data orchestration tools: Apache Airflow or similar supporting pipeline scheduling and management (preferred) Data visualization tools: Power BI, Tableau supporting reporting and dashboarding (preferred) Overall Responsibilities Design, develop, and optimize scalable data pipelines supporting enterprise analytics, data migration, and operational reporting within cloud environments Build and maintain large-scale data ecosystems supporting real-time and batch data processing supporting business insights Collaborate with data architects, data scientists, and business stakeholders to define requirements and translate them into efficient data workflows Support cloud migration, schema design, and data validation activities ensuring compliance with governance and security standards Monitor pipeline performance, troubleshoot failures, and optimize for data throughput and reliability Automate data ingestion, transformation, and validation workflows supporting continuous integration and delivery processes Support enterprise data strategy through schema management, data governance, and operational best practices Document data architecture, pipeline design, operational procedures, and security policies supporting audits and compliance Technical Skills (By Category) Languages & Frameworks (Essential): Python: supporting scripting, automation, and data transformation workflows Spark, Hive supporting big data processing and large dataset management SQL supporting data validation and query optimization in relational databases Data & Warehouse Management: Experience supporting data modeling, schema design, and data validation for data lakes/supporting large-scale data warehouses Cloud & Infrastructure: GCP supporting cloud-native data processing and migration (preferred) Automation support via Terraform or cloud provider-specific automation tools (preferred) Tools & Platforms: Data orchestration: Apache Airflow or equivalent supporting scheduling workflows (preferred) Visualization tools supporting operational dashboards and data reporting (preferred) Experience Requirements 4+ years of supporting enterprise big data pipelines in cloud environments Proven experience in designing, deploying, and optimizing data workflows supporting analytics and migration efforts Extensive hands-on experience supporting data validation, reconciliation, and security in enterprise data ecosystems Strong background supporting cloud migration, data lake/warehouse setup, and automation workflows (preferred) Experience working with large datasets, optimizing queries, and ensuring high data throughput in cloud environments supporting enterprise operations Day-to-Day Activities Develop, test, and support scalable data pipelines supporting enterprise analytics and migration projects Collaborate with data teams to refine data workflows, schemas, and pipeline performance Support data migration, environment setup, and data validation activities supporting compliance and operational resilience Troubleshoot pipeline failures, data quality issues, and performance bottlenecks supporting enterprise standards Automate data ingestion, processing, and validation workflows using cloud-native tools and automation frameworks Monitor data pipeline health, optimize throughput, and implement performance improvements supporting high-availability standards Document data architecture, pipeline workflows, and operational procedures supporting audits and enterprise data governance Qualifi

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