Position: Data Engineer - Architect

Open Positions: 1

Date: August 2026

Skillset: Azure, Databricks, Snowflake, Spark, PySpark, SQL, Python, ADF/Airflow, Data Modeling, CI/CD, Terraform/Bicep, Data Governance

Experience: 10 to 12+ Years

Requirement: Full-Time

Location: Hyderabad - Work from Office

Company Description:

At NStarX, we live by our mission: being the Lodestar to Success for enterprises navigating the rapidly evolving digital landscape. We build cutting-edge, production-ready platforms that integrate advanced AI, cloud-native architecture, and robust data engineering to solve complex business challenges at scale. If you thrive in a collaborative environment that values technical excellence, architectural ownership, and production-first innovation, NStarX is where your impact will shine.

Job Description:

We are looking for a Data Engineer - Architect who can bridge high-level data strategy with hands-on engineering. The role will own the design, architecture, governance, and technical direction of enterprise-grade data platforms using modern cloud and distributed-computing technologies. The ideal candidate will bring deep experience in Azure data architecture, Databricks/Snowflake, data modeling, orchestration, DevOps, Infrastructure-as-Code, security, performance optimization, and cloud cost management.

Key Responsibilities:

- System Architecture: Design scalable, resilient, secure, and cost-effective data lakehouses and warehouses using modern patterns such as Medallion Architecture (Bronze/Silver/Gold).

- Technical Leadership: Set engineering standards, conduct architecture and code reviews, establish reusable frameworks, and mentor data engineers on best practices.

- Data Enablement: Deliver reliable, high-performance data platforms that support BI, data science, machine learning, and operational analytics.

- Cloud Architecture: Design end-to-end Azure/cloud infrastructure blueprints covering storage, compute, RBAC, security, network isolation, scalability, and cost optimization.

- Big Data & Processing: Architect and optimize Spark/Databricks/Snowflake workloads, including Delta Lake design, cluster sizing, partitioning, Z-Ordering, Vacuuming, clustering, and performance tuning.

- Data Pipelines & Ingestion: Build and standardize production-grade PySpark, Python, and SQL pipelines supporting batch, micro-batch, streaming, and CDC ingestion patterns.

- Data Modeling: Establish organizational data-modeling standards and design Star/Snowflake schemas, Kimball dimensional models, Data Vault patterns, and Lakehouse storage layers.

- Orchestration: Architect workflow dependencies, retries, alerts, monitoring, and backfilling using Azure Data Factory, Airflow, or Databricks Workflows.

- DevOps & CI/CD: Implement Git-based development standards and automated CI/CD pipelines using Azure DevOps or GitHub Actions for reliable data-platform deployments.

- Infrastructure-as-Code: Design and implement infrastructure provisioning using Terraform, Bicep, or equivalent IaC frameworks.

- Governance & Security: Implement metadata catalogs, lineage, data quality controls, access policies, and regulatory compliance practices using technologies such as Unity Catalog and Microsoft Purview.

- Performance & FinOps: Benchmark workloads, resolve bottlenecks, optimize memory and compute configurations, monitor cloud consumption, and drive cost-efficiency across data platforms.

Skills and Experience:

- Overall Experience: 10 to 12+ years of IT/software engineering experience, with at least 5 years in Azure data architecture, cloud platform design, or equivalent data-platform architecture.

- Distributed Computing: 10+ years of experience working with distributed data processing or modern cloud data platforms, with strong hands-on exposure to Databricks, Spark, Snowflake, or equivalent technologies.

- Data Architecture: Strong understanding of Lakehouse/Warehouse architecture, Medallion Architecture, scalability, resiliency, security, and cost optimization.

- Data Modeling: Strong mastery of Kimball dimensional modeling, Star/Snowflake schemas, Data Vault, and analytical data design.

- Programming: Strong hands-on expertise in Python/PySpark and SQL, including production-grade transformations and performance optimization.

- Orchestration: Strong experience with Airflow, Azure Data Factory, Databricks Workflows, or equivalent orchestration platforms.

- DevOps: Hands-on experience with Git, CI/CD, Infrastructure-as-Code, Terraform/Bicep, and automated deployment practices.

- Cloud & Security: Strong understanding of Azure services, RBAC, identity, network isolation, security controls, monitoring, and enterprise governance.

- Communication & Leadership: Ability to communicate architecture decisions clearly, lead technical discussions, mentor engineers, and work effectively with cross-functional stakeholders.

Good to Have:

- Governance: Experience with Unity Catalog, Microsoft Purview, metadata management, data lineage, data quality, GDPR, HIPAA, or other regulated-data environments.

- Monitoring: Experience with Azure Monitor, Log Analytics, operational alerting, and platform observability.

- Cloud Breadth: Exposure to AWS or GCP data platforms in addition to Azure.

- Modern Data Engineering: Experience with CDC, streaming architectures, reusable ingestion frameworks, and platform engineering practices.