Data Engineer

data engineer

Key Responsibilities

• Design, develop, and maintain

  ETL/ELT pipelines

  using AWS Glue, Python, and SQL to integrate data from multiple sources (MySQL, MongoDB, Postgres, Redshift, APIs, and S3).
• Build and manage

  Power BI dashboards and reports

  for business teams (Sales, Marketing, Product, and Risk).
• Collaborate with stakeholders to translate business requirements into data models and visualizations.
• Optimize SQL queries, indexes, and ETL performance for large datasets.
• Implement

  data validation, monitoring, and logging

  to ensure quality and consistency across systems.
• Support

  AWS DMS

  replication pipelines and maintain data warehouse structures in Redshift.
• Participate in design reviews, documentation, and deployment of new data and reporting solutions.
• Ensure adherence to

  data governance

  ,

  security

  , and

  compliance

  policies (masking, encryption, and access control).

Required Skills & Experience

• 2–5 years

  of experience in

  data engineering and business reporting.

• Strong command of

  SQL

  and database performance tuning (MySQL, Postgres, Redshift).
• Hands-on experience with

  Power BI

  (DAX, Power Query, Data Modelling, Row-level Security).
• Proficiency in

  Python

  (Pandas, PySpark, boto3, etc.) for ETL scripting and data processing.
• Experience with

  AWS data services

  —Glue, DMS, S3, Lambda, and CloudWatch.
• Good understanding of

  data warehousing concepts

  , star/snowflake schemas, and reporting layers.
• Familiar with

  Git

  ,

  ETL workflow orchestration (Airflow/Glue Workflows)

  , and deployment processes.
• Excellent analytical, communication, and problem-solving skills.

Good to Have

• Experience in

  financial or lending domain

  data.
• Exposure to ADF,

  Athena, EMR, or Redshift Spectrum

  .
• AWS Certification in Data Analytics or equivalent.

Why Join Us

• Be part of a

  core data engineering team

  driving analytics and decision-making across Biz2X.
• Work on

  end-to-end data delivery

  —from ingestion to visualization.
• Exposure to

  large-scale financial and customer data ecosystems

  .