Data Architect

Job Description:

JOB SUMMARY

Looking for highly skilled and experienced Senior Data Architect. As a Senior Data Architect,
you will play a critical role in designing, implementing, and managing data architecture,
ensuring it supports both current needs and future aspirations in data and AI. This role
requires a deep understanding of data architecture principles, data modelling techniques,
data governance practices, and cloud-based data platforms. You will also need to have a
strong understanding of AI/ML concepts and how data architecture can support AI initiatives.
In this role, you will be a key player in enabling data-driven decision making, supporting
AI/ML initiatives, and ensuring data security and compliance. You will be responsible for
translating business requirements into technical specifications and collaborating with
stakeholders to ensure data solutions align with business objectives.

RESPONSIBILITIES
Data Architecture

Develop and maintain a comprehensive data architecture strategy that aligns with business
objectives and technological advancements.
Design and implement data models, including conceptual, logical, and physical models, to
ensure data consistency, integrity, and efficiency.
Define and enforce data governance policies, standards, and best practices to ensure data
quality, security, and compliance with relevant regulations.
Collaborate with data engineers to ensure the seamless integration of data from various
sources, including internal systems, external databases, and cloud-based platforms.
Optimize data storage and retrieval mechanisms to ensure high performance and scalability
of data systems.
Evaluate and recommend new data technologies and tools to enhance data architecture and
management processes.
Develop and maintain comprehensive documentation for data architecture, processes, and
standards.

AI/ML
Collaborate with data scientists to understand data requirements for AI/ML initiatives and
design data architectures that support model development and deployment.
Develop and implement data pipelines for data ingestion, processing, and transformation to
prepare data for AI/ML model training and inference.
Ensure data quality and consistency for AI/ML applications by implementing data validation
and cleansing processes.
Contribute to the development and implementation of MLOps practices to streamline model
deployment, monitoring, and management.
Ensure data quality, privacy, and compliance for AI/ML applications. This includes
responsibilities related to data lineage, data anonymization, and ethical considerations in AI.
Stay abreast of industry trends and best practices in AI/ML and data architecture to ensure
our solutions remain cutting-edge.

Collaboration
Establish and maintain strong collaborative relationships with data science and data
engineering leadership.
Participate in regular meetings with data science and data engineering teams to discuss
project requirements, data needs, and architectural solutions.
Collaborate on joint projects to ensure alignment between data architecture, data
engineering, and data science initiatives.
Share knowledge and expertise with data science and data engineering teams through
presentations, workshops, and documentation.
Provide technical guidance and support to data science and data engineering teams on data
architecture related issues.
Conduct Code reviews for data engineering and AI/ML projects, ensuring Code quality and
adherence to best practices.

Knowledge, Skills and Experience
Essential:
Experiences in developing, managing, scaling up complex Data platforms with Analytics
capabilities.
Experience: Proven experience as a Data Architect or similar role, with a minimum of 10
years in database design and data management.
Data Modelling: Strong understanding of data modelling concepts, techniques, and tools
(e.g., ERwin, ER/Studio).
Data Warehousing and ETL: Experience with data warehousing, ETL processes, and data
integration tools.

Proficiency in SQL and NoSQL databases including relational databases (ex. PostgresSQL,
MySQL), MongoDB etc.
Experience with Big data technologies like Apache Spark, Kafka and Hadoop.
Programming Languages: Strong experience with SQL, Python and Java.
Cloud Platforms: Knowledge of cloud-based data platforms and services (e.g., AWS, Azure,
GCP).
Analytical Skills: Excellent analytical and problem-solving skills with a keen attention to
detail.
Communication Skills: Strong communication and interpersonal skills, with the ability to
collaborate effectively with cross-functional teams.
AI/ML Experience : Experience with AI/ML concepts and data preparation for AI/ML is a
plus..