Senior Data Engineer

About the Project:

We are engaging with a key customer to build and operationalize their Enterprise Data Platform (EDP). This strategic initiative aims to mature their data and analytics capabilities across the organization. The project involves building a robust data platform, establishing a new operating model, and delivering foundational data products. You will be working closely with the customer to fill critical gaps in architecture, governance, security, and data product delivery, with a specific focus on Master Data Management (MDM) and comprehensive Data Quality
.

What You'll Do (Responsibilities):

As a Senior Data Governance & Platform Engineer, you will be a hands-on contributor across all facets of the project, specifically leading the implementation of master data and quality frameworks:

• Platform Foundations (Reference Architecture & DataOps):
  Collaborate on the design and implementation of a consolidated EDP reference architecture. You will be responsible for building out and hardening the platform's core components using Azure Synapse, Azure Databricks, and Azure Data Lake Storage (ADLS). You will help define and implement a DataOps model, including CI/CD pipelines, environments, and observability.

• Master Data Management (MDM) & Quality Leadership (NEW FOCUS):

MDM Solutioning:
Design, model, and configure the core components of the Master Data Management solution (Golden Record definition, matching/merging rules, hierarchy management) for critical domains (e.g., Customer, Product, Vendor).

Data Quality Automation:
Define, implement, and monitor automated data quality checks (completeness, conformity, accuracy) directly within the ingestion and transformation pipelines (Bronze to Gold layers).

Data Quality Scorecards:
Establish and maintain Data Quality scorecards and dashboards, reporting metrics to Data Stewards and business stakeholders via tools like Azure Purview.

• Data Governance & Catalog:
  Support the operationalization of the full data governance framework. This includes implementing data classification policies, setting up data quality scorecards, and configuring the data catalog with ownership and lineage in a tool like Azure Purview.
• Data Security & Access:
  Implement fine-grained access controls using patterns like Row-Level Security and Column-Level Security. You'll help design and apply a security model using Azure Entra ID groups and implement policy-as-code to protect PII.
• Data Product Pipeline Factory:
  Build and maintain scalable and repeatable data pipelines to ingest, transform, and publish data from various sources. You will work on creating a standardized framework for data products, ensuring observability and adherence to established
  MDM and DQ standards.
• Data Literacy & Change Management:
  Contribute to the enablement of data stewards, engineers, and analysts by providing practical, role-based training anchored to the data products and governance frameworks you build.

What We're Looking For (Required Skills & Experience):

We are seeking hands-on data engineers with a passion for building robust, well-governed, and secure data platforms. A strong candidate for this team will have:

• Deep Azure Data Services Expertise:
  Proven, hands-on experience with the Azure data ecosystem: Azure Synapse Analytics, Azure Databricks, Azure Data Lake Storage (ADLS), and Azure Purview. Experience with Unity Catalog is highly desirable.

• MDM & Data Quality Specialization:

Master Data Management:
Practical experience designing and implementing MDM solutions, including experience with MDM concepts (match, merge, survive, hierarchy management) and familiarity with MDM platforms (e.g., Microsoft MDS).

Data Quality Analyst Skills:
Expertise in defining, implementing, and monitoring DQ rules. Strong ability to profile source data, identify data issues, and track metrics like data freshness, accuracy, and completeness.

• Data Engineering Fundamentals:
  Strong background in data modeling (Dimensional, 3NF), ETL/ELT processes, and building scalable data pipelines for batch and streaming data. Knowledge of data warehousing and lakehouse architectures.
• Programming & Scripting:
  Proficiency in
  Python and PySpark is essential
  for data transformation and pipeline development in Azure Databricks. Strong SQL skills are also required.
• DevOps & CI/CD:
  Experience with DataOps principles, including setting up and maintaining CI/CD pipelines using tools like Azure DevOps or GitHub Actions. Familiarity with managing environments and infrastructure as code is a strong plus.
• Data Governance & Security:
  Practical experience or a solid understanding of data governance concepts, including data quality, lineage, metadata management, and access control models (RBAC/ABAC).

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 5+ years
  of progressive experience in a data engineering or data governance focused role.
• Excellent problem-solving, analytical, and communication skills.
• Demonstrated ability to work effectively in a collaborative and fast-paced environment.