Senior Data Architect

JD For Senior Data Architect
A Senior Data Architect is responsible for designing and governing the end-to-end data

architecture across an organisation, ensuring it aligns with business goals, performance

standards, and regulatory requirements. This role involves strategic oversight of data lake,

warehouse, transactions and pipeline architecture, focusing on scalability, interoperability,

security, and analytics readiness.

Key Responsibilities
Enterprise Data Architecture

• Define and maintain enterprise-wide data architecture standards, principles, and models.
• Design integrated data platforms (data lake, data warehouse, data marts) that support real-time and batch processing.
• Architects zoned data lakes (raw, curated, consumption) on platforms like AWS S3, Azure Data Lake, or GCP.

Data Modeling and Design

• Design conceptual, logical, and physical data models aligned to business requirements.
• Create standard models for structured, semi-structured (JSON, XML), and unstructured data.
• Lead schema optimization, indexing strategy, and storage format decisions (e.g., Parquet, ORC).

Platform Strategy & Integration

• Define strategy for integrating various data sources (ERP, CRM, APIs, third-party datasets) into centralized data architecture.
• Support hybrid integration across on-premise and cloud (AWS, Azure, GCP).
• Oversee selection and implementation of data engines (ClickHouse, Redshift, Snowflake, BigQuery, etc.)

Data Pipeline and ETL/ELT Oversight

• Guide the design and implementation of scalable and reusable data pipelines using Airflow, Spark, Kafka
• Standardize ingestion, transformation, and enrichment processes with quality and lineage checks.
• Oversee both batch and streaming ETL strategies and tool selection.

Data Governance and Quality

• Establish data governance frameworks including master data management (MDM), metadata, and lineage.
• Define and enforce data quality rules, validation logic, and exception handling workflows.
• Enable data cataloging and discovery (e.g., using AWS Glue Data Catalog,Apache Atlas, or Collibra).

Security, Compliance & Access Control

• Define security architectures and controls to protect sensitive data.
• Implement RBAC/ABAC models across tools (e.g., ClickHouse, Snowflake, etc.).

Performance and Scalability

• Evaluate and recommend storage engines and file formats based on use case(OLAP, realtime, archival).
• Architect for performance through proper indexing, partitioning, and caching strategies.
• Monitor and continuously optimize architecture for throughput, latency, and cost.

Stakeholder Collaboration and Leadership

• Partner with Data Engineers, BI Analysts, DevOps, and Product teams to align on technical solutions.
• Provide architectural oversight during data platform buildout and migrations.
• Lead data architecture reviews and technical assessments.

Skills and Expertise

Core Technical Skills
Data Architecture:
Expertise in designing zoned data lakes, warehouses, and federated models.

Modeling:
Proficiency in dimensional modeling , data vault, and third normal form (3NF).

Big Data Platforms:
ClickHouse, Redshift, Snowflake, BigQuery, Delta Lake, Hive.

ETL/ELT:
Airflow, Kafka, Spark, Glue, dbt, NiFi.

Cloud:
Deep experience with AWS (S3, Glue, Athena, Lake Formation), Azure Data Services, or GCP.

Programming & Query Languages

• SQL (Advanced), Python, Spark SQL, Bash.
• Familiarity with Scala or Java (desirable for Spark-based systems).

Experience Requirements

• years of experience in data engineering/architecture.
• Proven experience designing and implementing large-scale data platforms in enterprise environments.
• Strong experience in both batch and streaming architecture.

Category Tools & Platforms

• Data Lake AWS S3, AWS Data Lake, Delta Lake, Iceberg
• Data Warehouses ClickHouse, Redshift, Snowflake, BigQuery
• ETL/Orchestration Airflow, Glue, Spark, dbt, Kafka
• Governance & Catalog AWS Glue Data Catalog, Apache Atlas, Collibra
• Monitoring & Logging CloudWatch, Prometheus, Grafana, Datadog
• Visualization Tableau, Power BI, Superset