Data Architect – Data Engineering & Business Intelligence

Applied Data Finance; India; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Applied Data Finance is seeking an experienced Data Architect to lead the design, governance, and evolution of its enterprise data platform. You will set the architectural vision across Data Engineering, BI, Analytics, and AI-ready data platforms.

The role requires deep AWS data-ecosystem experience, including Redshift, S3, Iceberg, Spark, and modern DW technologies, with Snowflake/Databricks desirable. You'll collaborate with cross-functional teams to deliver scalable, reliable data solutions.

Qualifications

• 10+ years of experience designing, building and governing enterprise data platforms.
• Experience defining architecture standards and cloud data platforms.
• Leadership roles such as Data Architect or Principal Data Engineer.

Responsibilities

• Define and own enterprise data architecture strategy.
• Design conceptual, logical and physical data models; dimensional models and Data Vault.
• Lead adoption of Apache Iceberg on Amazon S3 Tables.
• Establish metadata standards and governance across the platform.
• Mentor engineers and promote engineering excellence.

Skills

Data Architecture

Data Modeling

Data Platform Engineering

Metadata & Governance

Business Intelligence

Education

Bachelor's or Master's in CS/IS/Engineering

Tools

Apache Spark

Apache Iceberg

AWS (Redshift, S3)

Tableau

Data Catalog tooling

Job description

We are seeking an experienced Data Architect – Data Engineering & Business Intelligence to lead and influence the design, governance, and evolution of our enterprise data platform. This role is responsible for defining the architectural vision across Data Engineering, Business Intelligence, Analytics, Metadata Management, Data Governance, and AI-ready data platforms.

The successful candidate will establish enterprise architecture standards, drive technology strategy, and design scalable, secure, and high-performing data solutions that power enterprise reporting, advanced analytics, machine learning, and future AI initiatives.

The ideal candidate will have deep expertise architecting enterprise-scale data platforms on AWS, with hands‑on experience in Amazon Redshift, Amazon S3 Tables (Apache Iceberg), Amazon EMR, Apache Spark, and modern data warehousing technologies. Experience with Snowflake and/or Databricks is highly desirable.

Working closely with Product Engineering, Business Intelligence, Analytics, Finance, Collections, and Portfolio Management Analytics teams, the Data Architect will translate business requirements into scalable, reliable, and high‑performing data solutions while driving engineering excellence across the organization.

What You'll Architect

• Enterprise Data Warehouse and Modern Lakehouse Platform.
• Apache Iceberg Lakehouse on Amazon S3 Tables.
• Enterprise Metadata Management and Data Governance.
• Enterprise Semantic Layer and KPI Framework.
• Scalable Data Platforms for Business Intelligence, Advanced Analytics, and AI.
• Enterprise Data Quality and Observability Frameworks.

Key Responsibilities

• Define and own enterprise data architecture strategy.
• Design conceptual, logical and physical data models.
• Design dimensional (Star/Snowflake) and Data Vault models.
• Establish enterprise architecture standards and reusable design patterns.
• Define standards for ingestion, transformation, orchestration and storage.
• Lead adoption of Apache Iceberg on Amazon S3 Tables.
• Optimize platform performance, scalability and reliability.

Metadata, Governance & Data Quality

• Define enterprise metadata strategy.
• Establish standards for Data Catalog, Business Glossary, Lineage, KPI Definitions and Data Quality.
• Lead architecture reviews and establish engineering standards.
• Review SQL, Python, Spark and ETL implementations.
• Define technology roadmap and evaluate emerging technologies.
• Partner with Product Engineering, BI, Analytics, Finance, Collections and Portfolio Management Analytics teams.
• Mentor engineers and promote engineering excellence.

Required Technical Skills

Data Architecture & Modeling: Enterprise Data Modeling, Dimensional Modeling, Data Vault, Semantic Layer, MDM, KPI & Metric Modeling.

Data Platform Engineering: Apache Spark (PySpark), Python, Advanced SQL, ETL/ELT, Batch & Streaming Processing, Pipeline Design, Performance Tuning.

Metadata & Governance: Apache Atlas (preferred), Metadata Management, Data Catalog, Business Glossary, Data Lineage, Data Stewardship, Data Quality.

Business Intelligence: Tableau, Semantic Layer, KPI Frameworks, Data Mart Design, Self‑Service Analytics.

Required Qualifications & Experience

• 10+ years of relevant experience designing, building and governing enterprise data platforms.
• Experience defining architecture standards and implementing cloud data platforms.
• Experience as Data Architect, Lead Data Engineer, Principal Data Engineer or similar technical leadership role.
• Strong mentoring and stakeholder management skills.

Education

Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, Mathematics or related discipline (or equivalent experience).

Preferred Qualifications

• Experience with Apache Atlas, DataHub, Collibra or Alation.
• Experience with data quality and observability frameworks.
• Financial Services/FinTech experience preferred.
• AWS, Snowflake or Databricks certifications preferred.

What Success Looks Like Within the First 12 months:

• Modernize the Lakehouse using Apache Iceberg on Amazon S3 Tables.
• Standardize metadata, semantic layer and KPI definitions.
• Improve platform scalability, reliability and cost efficiency.
• Strengthen engineering quality and mentoring.
• Enable an AI‑ready enterprise data platform.