Data and Integration Engineer

Data and Integration Engineer would be responsible for managing data and integration capabilities across WSP Digital Products, including structured, semi-structured, and unstructured data. The role would design and support data ingestion, validation, transformation, reconciliation, and integration flows across product systems, enterprise platforms, APIs, cloud services, and external data sources. The engineer would ensure data quality, integration reliability, secure data movement, and traceability across current and future products.

Data Management & Data Understanding Manage structured, semi-structured, and unstructured data used across digital product platforms. Analyze source data formats including SQL tables, JSON, XML, CSV, documents, logs, images, geospatial files, and API payloads. Define data mapping, metadata, lineage, validation rules, and data quality expectations. Support reporting, analytics, operational dashboards, product insights, and downstream data requirements. Data Ingestion, Validation & Processing Design and support batch, scheduled, and near-real-time data ingestion workflows from internal and external sources. Build validation, reconciliation, deduplication, enrichment, transformation, and error-handling routines. Support ETL/ELT processes, file processing, document ingestion, API-based data exchange, and cloud data movement. Monitor ingestion health, failed loads, schema changes, data anomalies, and data completeness. Integration Engineering Build and maintain APIs, connectors, integration jobs, scripts, and service-based data exchange workflows. Integrate product platforms with enterprise systems, external providers, GIS/geospatial platforms, identity services, and data stores. Support event-driven, API-based, file-based, and scheduled integration models based on product needs. Work with architects and developers to define integration contracts, error handling, retry logic, monitoring, and operational support requirements. Data Quality, Support & Governance Define and maintain data quality checks, reconciliation reports, validation dashboards, and exception-handling processes. Participate in root-cause analysis of data defects, integration failures, ingestion errors, and downstream inconsistencies. Support migration, onboarding, configuration, and release activities involving data and integrations. Maintain documentation for data flows, integration dependencies, transformation logic, and support procedures. Required Technical Skills Data Engineering | Advanced — data ingestion, transformation, validation, reconciliation, structured and unstructured data handling Integration Engineering | Advanced — REST APIs, connectors, service integrations, scheduled jobs, event-driven flows, and data exchange patterns Databases & Querying | Proficient — SQL, PostgreSQL / SQL Server, relational modelling, query tuning, and data profiling Cloud Data Services | Proficient — Azure Data Factory, Logic Apps, Functions, Blob Storage, queues, or equivalent cloud data tooling Data Quality | Advanced — validation rules, exception handling, anomaly detection, lineage, completeness, and reconciliation checks Programming / Scripting | Proficient — Python, C#, SQL, PowerShell, or equivalent scripting for automation and integrations Required Experience Minimum 6 years of professional experience in data engineering, integration engineering, ETL development, API integration, or enterprise data platforms. Experience handling structured, semi-structured, and unstructured data across enterprise applications or digital products. Experience building or supporting ingestion pipelines, validation routines, reconciliation checks, and operational data monitoring. Experience in working AI/ML frameworks and data pipelines Hands-on experience with databases (PostgreSQL, MongoDB, SQL/NoSQL) Strong experience with SQL databases, API-based integrations, file-based integrations, and cloud-hosted data movement. Experience troubleshooting data defects, ingestion failures, integration issues, schema mismatches, and downstream data inconsistencies. Preferred: Experience with Azure Data Factory, Azure Functions, Logic Apps, Event Grid, Service Bus, Blob Storage, or equivalent tools. Exposure to GIS/geospatial data, document-heavy platforms, environmental datasets, or engineering data sources. Experience supporting data migration, platform transition, MVP stabilization, or SaaS product integrations. Exposure to data governance, metadata management, lineage, data quality reporting, and data observability.

Bachelor’s degree in Computer Science , Information Technology, Data Engineering, Engineering, or related field . Azure Data Engineer, cloud data platform, integration, or database certification is preferred but not mandatory. BGV Employment with WSP India is subject to the successful completion of a background verification ("BGV") check conducted by a third-party agency appointed by WSP India. Candidates are advised to ensure that all information provided during the recruitment process — including documents uploaded — is accurate and complete, both to WSP India and its BGV partner.