Job Summary
GCP Senior Data Engineer
Responsible for leading discovery and analysis across in-scope source systems, assuming 35–50 source tables per domain with up to 2 years of historical data.
Design, build, and maintain scalable data pipelines on GCP to support reporting and analytics use cases.
Develop and optimize data models and curated datasets to support up to 10 reports per domain, with total reporting tables ranging from 40 to 100 depending on complexity.
Perform historical data loads and incremental processing ensuring data accuracy, performance, and reliability.
Lead implementation of ELT/ETL pipelines for migrating legacy reporting solutions, including conversion of approximately 100 SSRS reports to Power BI datasets and data models, and migration of SAP BO assets to Power BI.
Support enhancement and optimization of 18 MicroStrategy cubes and 10 dashboards, ensuring data consistency and performance improvements.
Rework and optimize 25 complex Power BI queries, applying best practices for DAX, query folding, and dataset performance.
Build Power BI datasets and reports for environments ranging from simple (5 source tables) to complex (40–100 tables).
Support Looker proof-of-concept activities by preparing curated data layers, validated datasets, and performant views to evaluate business and technical fit. Contribute to reporting tool rationalization and retirement initiatives by ensuring parallel run support, data validation, and smooth cutover to modern BI platforms.
Participate in discovery, assessment, recommendation, and roadmap activities for scoped datasets (including 5-table assessments), providing effort estimates, technical approaches, and sequencing guidance.
Ensure adherence to cloud-native best practices on GCP for performance optimization, cost efficiency, data quality, security, and scalability.
Implement data validation, reconciliation, and logging frameworks as part of engineering deliverables.
Work closely with architects, BI developers, and business stakeholders to translate reporting requirements into reliable data engineering solutions.
Account for non-development activities such as documentation, testing, code reviews, coordination, and operational readiness, recognizing overheads of approximately 50% of overall development effort
Key Responsibilities
2. To conduct comprehensive code reviews, establish and oversee quality assurance processes, performance optimization , implementation of best practices and coding standards to ensure successful delivery of complex projects.
3. To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility study (technical alternatives, best packages, supporting architecture best practices, technical risks, breakdown into components, estimations).
4. To collaborate with stakeholders to define project scope, objectives, deliverables and accordingly prepare and submit status reports for minimizing exposure & closure of escalations.