Data Engineer

Data Engineer – Mulehunter.ai

Job Location

Mumbai, Maharashtra (On-site / Hybrid)

About Mulehunter.ai & Why This Role Matters

Mulehunter.ai is an advanced AI/ML-based system designed to detect mule accounts across the banking ecosystem. Reliable, well-structured data pipelines are the critical foundation on which our modeling, alerting, and reporting workflows operate.

In this role, you will design and execute robust data workflows to support advanced analytics and machine learning initiatives. Operating from our Mumbai office, you will collaborate closely with data scientists and business stakeholders to manage, transform, and operationalize large-scale financial and transactional datasets.

What You Will Work On

Data pipelines and infrastructure for Mulehunter.ai, managing large-scale banking datasets built to power AI/ML fraud detection at scale.

What You Will Do

Data Integration: Extract structured and unstructured data from diverse banking databases, ensuring secure, high-throughput, and efficient data flow into modeling pipelines.

Data Preprocessing: Clean raw financial datasets, handle missing values, normalize formats, and prepare complex account-level and transaction-level data for downstream ML applications.

BRE Query Development: Design and implement optimized SQL-based queries and logic for the Business Rule Engine (BRE) based on exploratory data analysis (EDA) insights.

ML Collaboration: Partner tightly with the Data Science team to support complex data preparation, automated feature engineering, and live ML experiments.

Pipeline Management: Build, maintain, schedule, and monitor production-grade ETL/ELT workflows to guarantee system reliability.

Security & Compliance: Enforce data security protocol, strict access controls, and complete data auditability at every layer of the data pipeline.

What We Are Looking For

Experience: 3–8 years of core experience in data engineering, preferably within the BFSI, financial crime, or AML/fraud domains.

Core Technical Skills: Strong mastery of SQL, advanced data query optimization, and proficiency in Python for data manipulation (pandas, NumPy, etc.).

Database Systems: Hands-on experience with relational databases (PostgreSQL, MySQL, Oracle) and data warehousing solutions (Snowflake, Redshift, BigQuery, or Azure Synapse).

Big Data & Orchestration: Solid experience with big data frameworks (Apache Spark, Hadoop) and pipeline automation/orchestration tools (Kafka, Apache Airflow).

Domain Understanding: Prior exposure to handling highly sensitive financial datasets, specifically account-level and transaction-level ledger data.

What Success Looks Like

Robust Infrastructure: Production-grade, highly reliable data pipelines consistently serving the machine learning modeling team.

Data Readiness: Clean, well-modeled, and structured datasets readily optimized for real-time analysis and model training.

Scalable Performance: Efficient, low-latency data infrastructure engineered to support high-volume fraud detection across the banking ecosystem.

Pay: ₹510,440.24 - ₹1,868,453.81 per year

Benefits:

Health insurance

Paid sick time

Provident Fund

Work Location: In person