Data and Integration Engineer

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.