Data Architect

Position Overview:

designing, implementing, and managing the data architecture

Key Responsibilities:

• Design and implement

  end-to-end data architecture solutions

  to support business intelligence, analytics, and data science needs.
• Define

  data standards, governance policies, and data modeling best practices

  .
• Develop

  conceptual, logical, and physical data models

  and ensure consistency across systems.
• Design and manage

  data integration processes

  , including ETL/ELT pipelines.
• Collaborate with software engineers, analysts, and stakeholders to understand data needs and translate them into architecture solutions.
• Oversee

  data storage, warehousing, and data lake

  strategies (on-premises or cloud).
• Implement

  data quality frameworks

  and

  metadata management systems

  .
• Ensure

  data security, privacy, and compliance

  with regulations (e.g., GDPR, HIPAA).
• Work with

  cloud data platforms

  such as AWS, Azure, or GCP for scalable data solutions.
• Evaluate and recommend new

  data technologies, tools, and platforms

  .
• Optimize

  data workflows, queries, and database performance

  .
• Document data flows, architecture diagrams, and system dependencies.

Required Skills & Qualifications:

• Bachelor’s or Master’s degree in

  Computer Science, Information Systems, Data Engineering, or a related field

  .
• Strong expertise in

  SQL

  and

  NoSQL

  databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra).
• Proficiency with

  data modeling tools

  (e.g., ERwin, ER/Studio, Lucidchart).
• Hands-on experience with

  ETL/ELT tools

  (e.g., Informatica, Talend, Apache NiFi, dbt).
• Experience with

  cloud data platforms

  — AWS (Redshift, Glue), Azure (Synapse, Data Factory), or GCP (BigQuery, Dataflow).
• Familiarity with

  data warehousing

  and

  data lake architectures

  .
• Understanding of

  data governance

  ,

  master data management (MDM)

  , and

  metadata standards

  .
• Knowledge of

  Python, Spark, or Hadoop ecosystem

  is a plus.
• Excellent problem-solving, analytical, and documentation skills.
• Strong communication skills for working with both technical and non-technical stakeholders.

Preferred Qualifications:

• Certifications such as:

• AWS Certified Data Analytics – Specialty

• Google Professional Data Engineer

• Microsoft Certified: Azure Data Engineer Associate

• DAMA Certified Data Management Professional (CDMP)

• Experience implementing

  modern data architectures

  (Data Mesh, Data Fabric).
• Familiarity with

  real-time streaming platforms

  (Kafka, Kinesis).
• Experience in

  AI/ML data pipeline architecture

  .
• Background in

  enterprise-level data strategy

  or

  consulting

  .