Architect - Data Engineering (Location: INDIA, Hybrid) at Aubrant Digital

, , India -

Full Time

Start Date

Immediate

Expiry Date

04 Aug, 26

Salary

0.0

Posted On

06 May, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Azure Databricks, PySpark, Spark SQL, Delta Lake, Data Modeling, Airbyte, Azure Synapse, Cosmos DB, Terraform, CI/CD, Medallion Architecture, ETL/ELT, Data Quality Engineering, Stakeholder Management, Agile, Technical Leadership

Industry

Software Development

Description

Position Overview The Architect owns the end-to-end delivery of the data engineering work on a flagship multi-year enterprise data transformation program. The program is building a unified, governed data foundation on Azure across multiple business domains, with real-time CDC ingestion, master data management, and AI-ready analytics. This is a builder-leader role: you act as the technical bridge between the customer's senior technology leadership and the Aubrant delivery team, write modeling decisions, get hands-on with Databricks and pipeline code as you lead a data engineering team, and pressure-test the team's QA approach yourself. Delivery Ownership & Execution Own end-to-end delivery of the data transformation against agreed architecture, requirements, and schedule Translate the architecture and Unified Data Model into an executable plan: source onboarding, ingestion patterns, ELT design, serving patterns, and quality gates Drive sprint planning, milestone tracking, and execution across the program's phased delivery Identify risks, dependencies, and blockers early; drive resolution and manage scope and timeline commitments Customer & Stakeholder Engagement Act as the day-to-day technical point of contact for customer leadership and engineering on progress, blockers, decisions, and solution alternatives Run technical working sessions, design reviews, and walkthroughs that move decisions forward Translate business context into technical implications, and technical complexity into clear leadership-ready summaries Architecture, Modeling & Engineering Hold a working understanding of the full target tech stack and validate that implementation choices stay consistent with the reference architecture Lead and contribute to data modeling across the core enterprise domains; review modeling work for identity, SCD, CDC, PII, and survivorship correctness Build production-grade ETL/ELT pipelines on Azure Databricks (PySpark, Spark SQL) with Delta Lake: ingestion, conformance, survivorship, and quality test layers Configure and extend Airbyte connectors for CDC ingestion and integrate API-based sources across SaaS, ERP, HRIS, and operational systems Apply Aubrant Workbench accelerators to compress build time and ensure consistency Infrastructure, DevOps & Quality Partner with the cloud and DevOps team on what the data team needs from the platform: workspace topology, network and identity, secret management, observability, and cost guardrails Ensure CI/CD pipelines for data assets are in place and used: unit and integration tests, lineage validation, environment promotion, automated deployment, and infrastructure-as-code discipline Define the QA approach: data quality rules, test data strategy, regression testing, reconciliation against sources, and acceptance criteria for golden records Instruct and review QA work; hold the line on quality gates between Bronze, Silver, Gold tiers and Dev, Test, Prod environments Team Leadership & Coordination Lead and coordinate a cross-functional pod including: Data Architects Data Modeler Senior Cloud Engineer Data Engineers QA Engineers Support Agile ceremonies, backlog prioritization, and remove blockers Mentor Studio Members and codify reusable patterns into the Studio knowledge base and the Aubrant Workbench Key Qualifications Experience 12+ years in data engineering and data platform delivery, with 5+ years in a Technical Lead or equivalent role on customer-facing engagements Multiple end-to-end deliveries of enterprise-scale data platforms, with a track record of delivering against architecture, schedule, and quality Required Technical Skills Azure Databricks (PySpark, Spark SQL), Delta Lake, the Medallion architecture, and ADLS Gen2: hands-on production experience Data modeling: conceptual, logical, and physical, including SCD strategy, CDC patterns, PII classification, and survivorship CDC and ingestion: production experience with Airbyte, Fivetran, Azure Data Factory, or equivalent, plus API-based source onboarding At least one of Azure Synapse, Cosmos DB, or Azure SQL Managed Instance for serving patterns CI/CD for data assets and infrastructure-as-code (Terraform, Bicep, or ARM) QA approach design and data quality engineering for enterprise data platforms Leadership & Communication Customer-facing presence: able to run a technical conversation with a VP of Technology and walk out with a decision Strong written technical communication: design memos, decision logs, and runbooks Demonstrated ability to mentor engineers and grow technical capability in a team Preferred Qualifications Databricks Certified Data Engineer Professional or Microsoft Azure Data Engineer Associate / Solutions Architect Expert Microsoft Purview or comparable governance and catalog tooling (Collibra, Atlan, Unity Catalog) MLflow lifecycle experience or GenAI / LLM integration patterns in production Exposure to regulated, compliance-heavy industries (HIPAA, SOC 2, GDPR, PCI DSS) Bachelor's or Master's degree in Computer Science, Engineering, or related field Built on Azure Databricks, Delta Lake, ADLS Gen2, Airbyte, Microsoft Purview, Azure Synapse, Cosmos DB, MLflow, and Power BI.

Responsibilities

Lead the end-to-end delivery of a multi-year enterprise data transformation program on Azure, acting as the technical bridge between senior leadership and the delivery team. Responsibilities include designing data models, building production-grade pipelines, and managing a cross-functional team of engineers and architects.