Data Engineer - Python

The candidate should also have good knowledge of ETL design and be able to develop optimized code. Working experience on PySpark and Scala programming is an added advantage.

Responsibilities

· Conduct detailed validation of functional specifications (eventually contribute to functional specifications if needed).

· Initiate, build, and contribute to technical specifications.

· Perform technical and/or data analysis to elaborate technical Specifications documents for the different IT stakeholders (IT 2S data provider, CIB datahub, AML dev teams)

· Assist the technical stakeholders to validate the solutions and validate the technical tests planned.

· Coordinate with the different stakeholders to implement the changes/evolutions in the delay, cost and quality expected.

· Control the completion of the technical tests and associated deliverables.

· Support and contribute to the releases organization.

· Ensure data ingestion controls and technical tests automation developments are done according to expectations

• Implement DevOps practices to ensure efficient and reliable deployment of data pipelines and ETL processes,

Direct Responsibilities

• Understand business requirement from business analysts, users and should have analytical mind to understand existing process and purpose better solutions
• Work on TSD designs, development, testing, deployment, support
• Suggest and implement innovative approach.
• Should be adaptable to new technology or methodology

Contributing Responsibilities

• Contribute towards knowledge sharing initiatives with other team members
• Contribute documentation of solutions and configurations of the models

Technical & Behavioral Competencies

Mandatory

3+ years of experience in Corporate and Institutional Banking IT, with a full understanding of the Corporate Banking and/or Securities Services activity.

Good understanding of AML monitoring tools and data needed for AML detection models.

Good understanding of Data Analysis and Data Mapping processes.

Extensive experience in working with functional and technical teams, defining requirements (mainly technical specification), establishing technical strategies, and leading the full life cycle delivery of projects.

Experience in Data-Warehouse architectural design providing efficient solutions in Compliance AML data domains.

Good Experience in Python developments, Oralce PL/SQL development

Excellent communication skills with the ability to explain complex technical issues in a simple concise manner.

Strong coordination and organizational skills.

Multi-tasking capabilities

All these qualifications are a plus:

Knowledge of Corporate Banking and Securities Services transactional data sources, flowing through the Compliance and Regulatory frameworks is a plus.

Knowledge of Swift message and/or MX message formats and relevance to AML monitoring

Experienced in implementing various data lineage mechanisms to meet regulatory requirements.

Success in the role is heavily dependent on the ability to show leadership, proactivity, and work cooperatively with both functional and technical teams, and offshore

• Specific Qualifications:

Python, Oracle,

Skills Referential (Required knowledge, skills and abilities)

• Technical Skills:

Programming & Scripting (Primary)

Advanced Python (3.x) – OOP, typing, async, performance profiling

Familiarity with Python data‑engineer libraries: pandas, pyarrow, sqlalchemy, cx_Oracle, oracledb,polars, duckdb

Shell scripting (bash, PowerShell) for automation and orchestration

Oracle Database Expertise (Primary)

Oracle Database (11g/12c/19c/21c) administration basics

SQL proficiency: complex queries, analytic functions, hierarchical queries, PL/SQL development

Data modeling (ER, dimensional) and schema design for OLTP & OLAP

Performance tuning: indexing, partitioning, optimizer hints, AWR/ASH analysis

• **Oracle Data Pump, SQL*Loader, External Tables

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Data Integration & ETL (Primary)

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Design and implementation of ETL/ELT pipelines in Python (e.g., polars,pandas, pySpark, dbt)

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Knowledge of messaging/streaming (Kafka, RabbitMQ) for real‑time ingestion

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Data orchestration platforms: Apache Airflow, Prefect, or Azure Data Factory

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Big Data & Distributed Processing (Secondary)

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Working knowledge of Apache Spark (PySpark) and its integration with Oracle

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Experience with cloud‑based big‑data services (AWS EMR, Azure Synapse, GCP Dataproc)

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Cloud & DevOps (Secondary)

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Oracle Cloud Infrastructure (OCI) services: Autonomous DB, Object Storage, Functions

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Containerization (Docker) and orchestration (Kubernetes) for scalable pipelines

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CI/CD pipelines (Git, Jenkins, GitHub Actions) for automated testing and deployment

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Infrastructure‑as‑Code tools (Terraform, OCI Resource Manager)

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Data Quality & Governance (Primary)

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Implementing data validation, profiling, and cleansing in Python

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Familiarity with data lineage, metadata management, and catalog tools (Apache Atlas, Collibra)

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Understanding of GDPR, CCPA, and other data‑privacy regulations

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Testing & Monitoring (Primary)

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Unit & integration testing frameworks (pytest, unittest) for data pipelines

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Monitoring & alerting (Prometheus, Grafana, OCI Monitoring) of ETL jobs and database health

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Version Control & Collaboration (Secondary)

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Proficient with Git (branching, pull‑requests, code reviews)

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Agile methodologies (Scrum/Kanban) and ticketing systems (Jira, Azure Boards)

Behavioral Skills:

- - Ability to collaborate / Teamwork

• Communication skills - oral & written
• Creativity & Innovation / Problem solving
• Ability to share / pass on knowledge

Education Level:

Bachelor Degree or equivalent

Location:** Bangalore