GCP Data Platform Engineer maintaining and optimizing production data platforms for Innodata, a global data engineering and AI services company. Supporting Airflow pipelines, GCP infrastructure, reliability, and troubleshooting.
About the role
- Data Engineer responsible for designing and maintaining data pipelines to support analytics and AI initiatives. Collaborating with various stakeholders to ensure high data quality and efficiency.
Responsibilities
- Design, build, and maintain batch, incremental, and file-based data pipelines to support analytics, reporting, and operational use cases.
- Support data migration initiatives, including the movement of data from legacy systems to modern platforms, ensuring continuity, accuracy, and reconciliation.
- Work with enterprise data integration tools such as Talend, including maintaining existing Talend jobs and supporting the migration or re-implementation of Talend pipelines into other platforms where required.
- Establish and maintain a robust data engineering environment (e.g., dev/test/prod separation, access controls, naming standards, source control, deployment processes, and monitoring) that enables reliable delivery and safe change management.
- Support ingestion, storage, and management of unstructured and semi-structured datasets (e.g., documents, PDFs, text extracts, files, metadata) alongside traditional relational data.
- Support and optimize data models in Power BI dashboards and AI-enabled analytics.
- Prepare and curate analytics- and AI-ready datasets, including clean, well-defined tables and reference data used in automation and AI-enabled solutions.
- Collaborate closely with AI & Automation Specialists to ensure data requirements for AI initiatives are well understood, properly sourced, validated, and production-ready.
- Translate business and reporting requirements into scalable data models, transformations, and pipeline designs.
- Develop and maintain trusted datasets that support dashboards, scorecards, client reporting, and downstream analytics.
- Implement data quality checks, validation logic, reconciliation processes, and monitoring to ensure data reliability and consistency.
- Troubleshoot and resolve data issues across ingestion, transformation, and consumption layers, identifying root causes and remediation actions.
- Develop and maintain clear documentation, including source-to-target mappings, data definitions, data lineage, and known limitations.
- Work in partnership with analytics, IT, data governance, and security stakeholders to ensure data solutions comply with privacy, security, and regulatory expectations.
- Contribute to the prioritization, planning, and delivery of multiple data engineering, migration, and enablement initiatives in parallel.
Requirements
- Bachelor’s Degree in Computer Science, Information Technology, Data Analytics or related field
- 4+ year experience working in data engineering, analytics engineering, business intelligence, or data platform roles.
- 4+ years Experience with data integration and ETL/ELT tools (e.g., Talend, Azure Data Factory, or similar enterprise platforms).
- Experience with Microsoft Power Automate (or similar workflow automation tools) to orchestrate process automation, notifications, and data movement/integration.
- Proficiency with Python (or similar scripting languages) for data transformations, automation, API integrations, and lightweight tooling.
- Understanding of data orchestration and scheduling patterns, including dependency management, retries, and operational runbooks for production pipelines.
- Experience implementing data quality and observability practices (e.g., validation rules, monitoring/alerting, SLAs) to ensure reliable and trusted datasets.
- Working knowledge of secure data handling and privacy-by-design principles (e.g., least-privilege access, encryption, PHI/PII considerations) when building and operating data pipelines.
- Strong SQL experience for data transformation, validation, reconciliation, and performance tuning.
- Experience with Power BI (data modeling, DAX fundamentals, and performance considerations) to support scalable dashboards and self-serve analytics.
- Experience working with both structured and unstructured datasets, including file-based or document-oriented data sources.
- Familiarity with relational and cloud-based data platforms used for analytics and reporting.
- Experience supporting data migrations, legacy system consolidation, or platform modernization initiatives.
- Understanding of data quality, documentation practices, and foundational data governance principles.
- Strong analytical and problem-solving skills, particularly in diagnosing data quality issues and pipeline failures.
- Ability to collaborate effectively with both technical and non-technical stakeholders, including analytics and AI delivery teams.
- Strong written and verbal communication skills, with the ability to clearly document data flows, dependencies, and assumptions.
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Remote work options
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