Data Architect

This role is for one of the Weekday's clients

Data Architect

Requirements

Key Responsibilities

• Lead the design and implementation of enterprise-level data architecture, including data pipelines, data models, and analytical frameworks.
• Build and optimize data ingestion, transformation, and processing pipelines using PySpark, Spark SQL, and distributed computing technologies.
• Architect and maintain data lakes, data warehouses, and streaming-based data solutions, ensuring efficiency, performance, and scalability.
• Collaborate with cross-functional teams including data engineering, analytics, cloud infrastructure, and business stakeholders to define data roadmaps, standards, and governance practices.
• Establish data modeling standards and best practices for structured, unstructured, and semi-structured data.
• Drive data quality, metadata management, and master data management efforts to ensure data consistency and integrity.
• Evaluate, recommend, and integrate new data management tools, platforms, and technologies aligned with industry best practices.
• Ensure security, compliance, and privacy requirements are embedded into data solutions, supporting regulatory frameworks and audit processes.
• Mentor and guide data engineers and analysts on architectural standards, performance tuning, and advanced data processing techniques.
• Conduct performance tuning, optimization, and troubleshooting of large-scale distributed data systems and pipelines.
  

Required Skills & Experience

• 7-13 years of experience in data architecture, data engineering, or related fields.
• Strong programming skills in Python and expert-level proficiency in PySpark / Apache Spark.
• Proven experience designing and implementing large-scale data pipelines and distributed data processing systems.
• Hands-on experience with big data ecosystems (e.g., Hadoop, Spark, Hive, HDFS, Kafka).
• Experience with cloud data platforms such as AWS, Azure, or GCP (e.g., Redshift, Snowflake, Databricks, BigQuery).
• Strong understanding of ETL/ELT frameworks, workflow management tools, and orchestration systems (Airflow, DBT, etc.).
• Deep knowledge of data modeling techniques including relational, dimensional, and NoSQL model designs.
• Familiarity with data governance, data quality frameworks, and metadata management.
• Excellent problem-solving, analytical thinking, and communication skills with the ability to simplify complex data challenges.
• Ability to collaborate effectively with engineering, analytics, and business stakeholders.
  

Preferred Qualifications

• Experience working in product-led or high-growth technology environments.
• Knowledge of machine learning data preparation and real-time analytics.
• Exposure to API-based data consumption and microservices architecture