Data Engineer | SDE - 1

• Design, build, and maintain scalable data pipelines to ingest, process, and transform data from multiple retail sources (POS systems, e-commerce platforms, inventory databases, customer touchpoints).
• Develop and manage ETL/ELT workflows supporting both batch and real-time data processing.
• Create and optimize data warehousing solutions to consolidate data from disparate systems into unified repositories.
• Implement data quality checks, validation rules, and monitoring to ensure accuracy and reliability.
• Collaborate with data analysts, data scientists, and backend engineers to understand data requirements.
• Build well-structured data models to support analytics, reporting, and ML use cases (demand forecasting, customer segmentation, BI).
• Optimize query performance and manage indexing strategies for high-volume retail transaction data.
• Implement data governance practices and ensure compliance with data privacy and security regulations.

Required Qualifications

Experience

• 2-5 years of experience in data engineering, analytics engineering, or related roles.
• Proven experience building and maintaining data pipelines.

Technical Skills

• Strong proficiency in Python for data processing and pipeline development.
• Solid experience with SQL and relational databases (PostgreSQL, MySQL, or similar).
• Hands-on experience with ETL/ELT tools such as Apache Airflow, dbt, or Luigi.
• Experience with data warehouses (Snowflake, Redshift, BigQuery, Azure Synapse).
• Working knowledge of data processing frameworks (Spark, Pandas, or similar).
• Experience with cloud platforms (AWS, Azure, or GCP) and their data services.
• Strong understanding of data modeling, schema design, and dimensional modeling.

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication skills for working with non-technical stakeholders.
• Ability to work independently as well as collaboratively in agile teams.

Nice to Have

• Experience with streaming platforms (Kafka, Kinesis).
• Knowledge of containerization and orchestration tools (Docker, Kubernetes).
• Familiarity with infrastructure as code (Terraform).
• Experience with data visualization tools (Tableau, Power BI, Looker).
• Exposure to AI/ML data pipelines and feature engineering.
• Understanding of vector databases and RAG architectures for generative AI.
• Prior experience in retail, e-commerce, or supply chain analytics.