Hybrid Senior Data Architect

at Welocalize

• Job title: Senior Data Architect
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Amazon Redshift, AWS, BigQuery, Cloud, Matillion, Python, SQL
• Location requirements: Hybrid, Noida, India

About the role

• Data Platform Architect responsible for modernizing Welocalize’s data infrastructure and analytics environment. Driving technical standards while collaborating closely with engineering and governance leads.

Responsibilities

• Rapidly gain fluency with the current-state data infrastructure and absorb the design and assessment materials produced by prior consulting work, translating them into actionable technical and sequencing plans.
• Proactively engage business stakeholders (Finance, Operational Leadership, FP&A) to understand high-priority business intelligence, reporting, and financial reconciliation needs.
• Define, model, and translate complex business requirements into concrete data pipelines and robust transformation logic within dbt, ensuring data quality, consistency, and fitness-for-purpose for Power BI consumption.
• Lead the architectural design and implementation of data modeling, transformation, and orchestration standards using dbt, Redshift, Estuary, and Power BI.
• Partner with the Data Engineering Leader to plan and sequence modernization workstreams and ensure technical execution aligns with architectural standards. Additional India-based engineering resources will be positioned for deployment to this initiative.
• Contribute directly to core dbt model and test development, ensuring speed, architectural quality, and maintainability.
• Collaborate with the Director of Data Quality & Governance to embed data quality, lineage, and governance frameworks into the platform’s design—including the enablement of automated data quality testing, proactive monitoring, and standardized issue resolution workflows.
• Explicitly design transformation logic to reconcile financial and operational data discrepancies and establish a Single Source of Truth for key metrics and metadata elements.
• Drive the migration and modernization of legacy workflows (Matillion, Power BI DAX) into version-controlled, tested, and documented dbt models.
• Establish and enforce best practices for Git-based workflows, CI/CD pipelines, and documentation across the data platform. Ensure the platform’s ongoing evolution remains structured, well-documented, and responsive to business analytical needs.
• Own the framework and standards for platform documentation, ensuring models, transformations, and dependencies are consistently described, discoverable, and integrated with governance processes. Leverage automation and AI-assisted tooling (e.g., dbt auto-docs, lineage visualization, metadata capture) to streamline and scale documentation, lineage tracking, and quality reporting.
• Mentor and upskill engineers and analysts supporting the initiative, embedding platform standards and reducing key-person risk.
• Architect the semantic model to maximize discoverability, optimize performance, reduce Power BI Premium capacity strain, and enable self-service analytics for business users.
• Develop and maintain metadata documentation that clearly defines dimensions and metrics in ways that are easy for other members of the data, operations, and business teams to understand.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field strongly preferred; equivalent experience acceptable. Master’s degree or advanced certification in data science, data engineering or architecture is a plus.
• 7–10 years of progressive experience in data engineering, data architecture, or related technical disciplines, including at least 3 years leading or designing cloud-based data platform implementations (e.g., Redshift, Snowflake, BigQuery).
• Demonstrated success designing, implementing, and optimizing modern data stacks that include automated testing, documentation, and governance capabilities.
• Deep expertise in data modeling and transformation using dbt (or equivalent frameworks).
• Expert-level proficiency in SQL and data warehouse design principles.
• Proven ability to translate abstract business requirements into scalable, production-ready data models and dbt transformation logic.
• Strong proficiency in Python for automation, orchestration, and integration tasks related to data pipelines, testing, and CI/CD.
• Experience with real-time and batch ingestion tools such as Estuary, Fivetran, or Airbyte.
• Proficiency with Git-based development workflows, including branching, pull requests, and code reviews.
• Hands-on experience implementing CI/CD pipelines for data or analytics environments.
• Proven ability to lead complex technical initiatives and establish scalable engineering and documentation standards without formal managerial authority.
• Experience mentoring engineers and promoting consistent development best practices.
• Strong communication and collaboration skills, with the ability to engage both technical and business stakeholders.
• Familiarity with AWS data ecosystem (S3, Redshift, Glue, IAM) or comparable cloud platforms.