Job Summary

Data Discovery & Profiling

Lead discovery across source systems (35–50 tables/domain, up to 2 years of data) to assess data quality, lineage, and readiness. Define data quality rules, identify gaps/anomalies, and deliver profiling reports, data dictionaries, and remediation plans.

Data Quality Engineering & Governance

Implement end-to-end data quality frameworks on GCP using APIs and native services. Enforce validation, reconciliation, schema control, and consistency across pipelines, ensuring accuracy, auditability, and compliance.

Pipeline Development (ETL/ELT)

Design and build scalable GCP pipelines for data ingestion, transformation, and analytics. Ensure high performance and reliability with embedded data quality checks supporting reporting and BI use cases.

Data Modeling & Processing

Develop and optimize data models (40–100 reporting tables/domain). Support historical loads and incremental processing with strong focus on data accuracy, consistency, and performance.

Legacy Modernization & BI Enablement

Drive migration of legacy reporting platforms (SSRS, SAP BO, MicroStrategy) to Power BI and modern BI tools. Optimize datasets, queries, and reporting layers using best practices (DAX, query folding, performance tuning). Support Looker POCs and reporting rationalization with validated datasets and controlled cutovers.

Metadata, Lineage & Documentation

Maintain complete metadata, end-to-end lineage, and standard documentation to ensure traceability, transparency, and governance across all data assets.

Monitoring, Audit & Reliability

Implement robust logging, monitoring, and audit frameworks (including extensions to audit/XCom patterns). Enable proactive detection of data issues such as schema drift, volume changes, and data freshness gaps.

Agentic & AI-driven Data Quality

Leverage AI/automation for real-time data quality monitoring, anomaly detection, and pipeline health tracking. Enable auto-remediation for known issues and escalation for unknowns, ensuring only validated data flows to curated layers.

KPI Governance & Data Certification

Establish centralized KPI governance with certified datasets and consistent metric definitions. Ensure trusted, business-aligned reporting across domains.

GCP Best Practices & Performance

Ensure adherence to GCP best practices across scalability, security (IAM, encryption), performance, and cost optimization. Build resilient, SLA-driven data systems.

Stakeholder Collaboration & Delivery

Work closely with architects, BI teams, and business stakeholders to translate requirements into scalable data solutions. Support testing, documentation, code reviews, and production readiness.

Key Responsibilities

1. To be responsible for providing technical guidance to a team of developers, enhancing their technical capabilities and increasing productivity.
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.

Skill Requirements

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Other Requirements

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