Data Warehouse Engineer

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

* Strong 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

1. Design scalable Data Warehouse/Lakehouse architecture

2. Architect real-time, near-real-time & batch data pipelines

3. Design CDC, ETL/ELT, event streaming and data orchestration

4. Define Dimensional, Data Vault, Event-based & ML-ready data models

5. Build data architecture for Payments, Fraud, Risk, Reconciliation, Analytics & AI/ML

6. Establish Data Governance, Quality, Lineage, Security & Observability

7. Design solutions for PII, PCI DSS, encryption, masking & tokenization

8. Optimize data platforms for performance, scalability, reliability & cost

9. 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

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

2. GCP: BigQuery, Cloud Storage, Dataflow, Dataproc, Dataplex

3. Streaming: Kafka/MSK, Kinesis, Pub/Sub, Flink, Spark Streaming

4. ETL/ELT: dbt, Airflow/MWAA, Glue, Spark/PySpark

5. Analytics: Tableau, Power BI, Looker, QuickSight

Domain Preference

1. Candidates with Payments / FinTech / Banking / Financial Services experience are highly preferred, especially in:

2. Authorization • Settlement • Refunds • Chargebacks • Fraud/Risk • Reconciliation • Payment Orchestration • Merchant Processing • KYC • PCI DSS