Hiring: Senior Data Warehouse Engineer | Payments / FinTech

Experience: 810 Years

Location: Remote

CTC: Up to 30 LPA (based on last CTC only)

Shift: US Shift | 6:30 PM 3:30 AM IST

Role Overview

We are looking for a hands-on Senior Data Warehouse Engineer to build and operate scalable ETL/ELT, real-time, near-real-time, and batch data pipelines for an enterprise payment ecosystem.

The role involves working with high-volume transactional data and creating reliable datasets for Analytics, BI, Fraud Detection, Risk, and AI/ML.

Key Responsibilities

- Design and maintain scalable ETL/ELT & data pipelines
- Build real-time, streaming & batch pipelines
- Implement CDC from operational databases
- Process data from APIs, databases, logs, Kafka, merchant systems and payment providers
- Develop data models for transactions, merchants, customers, settlements, refunds & chargebacks
- Perform data cleansing, transformation, enrichment, aggregation and validation
- Build ML-ready datasets and feature-generation pipelines
- Implement data quality checks, monitoring and lineage
- Troubleshoot pipeline failures, data discrepancies and latency issues
- Optimize pipelines for performance and cost
- Implement partitioning, clustering and storage strategies
- Work with Data Architects, Data Scientists and Engineering teams
- Participate in code reviews and mentor junior engineers

Must-Have Skills
- 8–10 years in Data Engineering / Data Warehousing
- Strong SQL and relational/analytical database experience
- Strong programming in Python, Java or Scala
- Enterprise Data Warehouse / Lakehouse experience
- Real-time / near-real-time data pipelines




- ETL/ELT & distributed data processing
- Cloud platforms: AWS and/or GCP
- Robust Data Modeling knowledge
- Data Quality, Monitoring & Troubleshooting
- Experience with large-scale transactional data

Preferred Technologies

- AWS/GCP: Redshift, S3, Glue, Athena, EMR, Lake Formation, BigQuery, Dataflow, Dataproc
- Streaming: Kafka, MSK, Kinesis, Pub/Sub, Flink, Spark Structured Streaming, Kafka Connect, Debezium
- ETL/Orchestration: dbt, Airflow/MWAA, Glue, Spark, PySpark, CDC
- Databases: PostgreSQL, MySQL, Oracle, SQL Server, Snowflake, Databricks
- Data Modeling: Star/Snowflake Schema, Dimensional Modeling, SCD, Data Vault, Event-based Models, Medallion Architecture
- BI: Tableau, Power BI, Looker, QuickSight, Superset

Hiring: Data Warehouse Architect | Payments / FinTech

Experience: 15+ Years

Location: Remote

CTC: Up to 30 LPA (based on last CTC only)

Shift: US Shift | 6:30 PM – 3:30 AM IST

Role Overview

Looking for an experienced Data Warehouse Architect to design and lead enterprise-scale Data Warehouse/Lakehouse architecture for a high-volume payment platform.

Key Responsibilities

- Design scalable Data Warehouse/Lakehouse architecture
- Architect real-time, near-real-time & batch data pipelines
- Design CDC, ETL/ELT,



event streaming and data orchestration
- Define Dimensional, Data Vault, Event-based & ML-ready data models
- Build data architecture for Payments, Fraud, Risk, Reconciliation, Analytics & AI/ML
- Establish Data Governance, Quality, Lineage, Security & Observability
- Design solutions for PII, PCI DSS, encryption, masking & tokenization
- Optimize data platforms for performance, scalability, reliability & cost
- Provide technical leadership and mentor Data Engineering teams

Must-Have Skills

A. 15+ years in Data Engineering / Data Architecture / Enterprise Data Platforms

B. Strong experience with Data Warehouse/Lakehouse

C. High-volume transactional systems

D. Real-time / Streaming / Event-driven architecture

E. Strong Data Modeling & Enterprise Architecture

F. Experience with AWS and/or GCP

G. Strong knowledge of Kafka, Spark, Airflow, dbt, CDC/Debezium

H. Experience with data security, governance and regulatory requirements

Preferred Technologies

- AWS: Redshift, S3, Glue, Athena, Lake Formation, EMR
- GCP: BigQuery, Cloud Storage, Dataflow, Dataproc, Dataplex
- Streaming: Kafka/MSK, Kinesis, Pub/Sub, Flink, Spark Streaming
- ETL/ELT: dbt, Airflow/MWAA, Glue, Spark/PySpark
- Analytics: Tableau, Power BI, Looker, QuickSight

Domain Preference

- Candidates with Payments / FinTech / Banking / Financial Services experience are highly preferred, especially in:
- Authorization • Settlement • Refunds • Chargebacks • Fraud/Risk • Reconciliation • Payment Orchestration • Merchant Processing • KYC • PCI DSS

📌 Data Warehouse Engineer (Kolkata)
🏢 TAD International Business Services
📍 Kolkata