Data Engineering Architect – Azure (ADF / SQL / Cloud Data Platform)

Company: TESTQ Technologies Limited
Apply for the Data Engineering Architect – Azure (ADF / SQL / Cloud Data Platform)
Location:
Job Description:

Job Type: Permanent

Work Mode: Hybrid (2 Days from office)

  • Architect and design end-to-end Azure data engineering solutions (batch + near real-time) aligned to enterprise standards.
  • Define target state architecture for data ingestion, transformation, orchestration, and serving layers.
  • Lead architectural decisions around scalability, resiliency, performance, security, governance, and cost optimization.
  • Design, develop, test, and deploy Azure Data Factory pipelines following best practices (modular design, parameterization, reusability, CI/CD readiness).
  • Build robust ingestion and orchestration workflows using:
    • Mapping Data Flows / Wrangling Data Flows (where applicable)
  • Implement operational excellence: logging, alerting, retry patterns, failure handling, and idempotent design.

SQL Development & Optimization

  • Develop and optimize SQL queries and stored procedures to support ADF pipeline operations and downstream transformations.
  • Conduct query plan analysis and performance tuning (indexes, partitioning strategies, statistics, query rewrites).
  • Establish SQL coding standards and reusable patterns for transformation logic.

Troubleshooting & Analytical Problem Solving

  • Apply a strong analytical mindset to diagnose and resolve complex data integration issues across ingestion, transformation, orchestration, and storage layers.
  • Perform root cause analysis (RCA) for pipeline failures, performance degradation, data quality issues, and environment instability.
  • Design proactive monitoring dashboards and alerts for pipeline SLAs and data freshness.
  • Define and enforce best practices for:
    • CI/CD for ADF (Azure DevOps / Git-based workflows)
    • Infrastructure-as-Code (ARM/Bicep/Terraform—preferred)
    • Version control, code review, release management
  • Implement data governance patterns: metadata management, lineage, auditing, encryption, RBAC, key management, PII controls.
  • Collaborate with security/compliance teams to ensure enterprise adherence.

Leadership & Stakeholder Management

  • Act as a technical leader for data engineering squads; mentor and guide engineers on design patterns and implementation.
  • Translate business requirements into technical architecture and delivery plans.
  • Work closely with Product Owners, Data Analysts, Data Scientists, and Platform teams to ensure alignment.

Required Skills & Qualifications

Must-Have (Strong)

  • 12–16 years of overall IT experience with significant data engineering & architecture exposure.
  • Strong Azure Cloud Data Engineering and associated services architecture knowledge.
  • Deep hands-on experience with:
    • SQL – advanced querying, stored procedures, performance tuning
  • Strong troubleshooting skills for complex multi-system data issues.
  • Strong understanding of data architecture concepts:
    • Data lakes/lakehouse/warehouse, dimensional modeling, ELT/ETL patterns

Azure Ecosystem (Preferred / Good to Have)

  • Azure data services experience in one or more:
  • Monitoring & observability:
  • Security & identity:
  • CI/CD practices for data pipelines; Git branching strategies and release governance.

Behavioral / Soft Skills

  • Excellent analytical thinking and structured problem-solving.
  • Strong communication and stakeholder management skills.
  • Ownership mindset, ability to drive standards and influence cross-team adoption.
  • Proven mentoring/coaching ability.

Education

  • Bachelor’s/Master’s degree in Computer Science, Information Systems, or related field (or equivalent experience).

Nice-to-Have Certifications

  • Microsoft Certified: Azure Data Engineer Associate
  • Microsoft Certified: Azure Solutions Architect Expert

What Success Looks Like (Outcome Focus)

  • Highly reliable ADF pipelines with strong observability, error handling, and SLA adherence.
  • Measurable improvements in SQL performance and pipeline execution time.
  • Standardized architecture patterns adopted across teams.
  • Reduced incident rates through proactive monitoring and strong RCA discipline.

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Posted: March 27th, 2026