Requirement details :

Data Architect

Location : Pune / Hybrid

Experience : 12+ Years

Interview Process :

- Round 1: Virtual Technical Interview

- Round 2: Face-to-Face Interview

About Apexon :

Apexon brings over three decades of expertise across Artificial Intelligence, Data & Analytics, Digital Engineering, and Experience, with strong industry expertise in Banking & Financial Services, Healthcare, and Life Sciences. Apexon is backed by Goldman Sachs Asset Management and Everstone Capital.

Job Summary :

We are looking for an experienced Data Architect to design, govern, and implement enterprise-scale data architecture, data platforms, and analytics ecosystems supporting Business Intelligence, Advanced Analytics, Machine Learning, and Digital Transformation initiatives. The ideal candidate will have strong expertise in data modelling, data warehousing, cloud data platforms, data governance, integration architecture, and modern data engineering practices.

Key Responsibilities :

- Define and maintain enterprise data architecture strategy, standards, and roadmap.

- Design conceptual, logical, and physical data models.

- Architect and implement data warehouses, data lakes, and lakehouse solutions.

- Design and optimize ETL/ELT pipelines and data integration frameworks.

- Establish standards for data governance, metadata, Master Data Management (MDM), data quality, lineage, and security.

- Design scalable, cloud-based data platforms using AWS, Azure, or GCP.

- Provide architectural guidance for BI, Analytics, AI/ML, and Generative AI initiatives.

- Evaluate data technologies and develop performance optimization strategies.

- Lead architecture reviews, technical discussions, and governance forums.

- Mentor data engineers and developers on best practices, architecture principles, and design patterns.

- Prepare architecture documents, data dictionaries, standards, solution blueprints, and technical documentation.

Required Skills & Qualifications :

- 12+ years of experience in Data Architecture, Data Engineering, or Enterprise Data Management.

- Strong expertise in:

1. Data Modelling: ER, Dimensional, Star and Snowflake schemas

2. Data Warehousing

3. ETL/ELT Architecture

4. SQL and Database Design

5. Data Integration Patterns

6. Data Governance and Data Quality

- Hands-on experience with at least one major cloud platform:

1. AWS: Redshift, Glue, Athena, S3, Lake Formation, EMR

2. Azure: Synapse, Data Factory, ADLS, Databricks

3. GCP: BigQuery, Dataflow, Dataproc, Cloud Storage

- Experience with relational and NoSQL databases such as PostgreSQL, SQL Server, Oracle, MongoDB, Cassandra, and DynamoDB.

- Strong understanding of Apache Spark / PySpark.

- Proficiency in SQL and familiarity with Python or Scala.

- Proven experience designing high-volume, highly available, scalable enterprise data platforms.

Preferred Skills :

- Databricks, Snowflake, Delta Lake

- Apache Kafka / Apache Airflow

- MLOps and AI/ML data platform architecture

- Generative AI, Vector Databases, Semantic Layers, and RAG

- Terraform / CloudFormation / ARM

- CI/CD practices for data platforms

- Streaming, IoT, Graph Databases, or Knowledge Graphs

- BI tools such as Power BI, Tableau, Looker, or QuickSight

Preferred Certifications :

- AWS Certified Data Engineer

- Microsoft Certified: Azure Data Engineer Associate

- Google Professional Data Engineer

- Snowflake SnowPro Core

- Databricks Certified Data Engineer Professional

- TOGAF or other Enterprise Architecture certifications

Education :

- Bachelors or Masters degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.