Description

Key Responsibilities

Design, develop, and maintain robust ETL/ELT pipelines for structured and semi-structured data.

Ingest data from APIs, databases, files, and streaming sources.

Build scalable data transformation workflows using Python, SQL, and Spark.

Develop and optimize data warehouse tables, data models, and analytical datasets.

Implement batch and real-time data processing solutions.

Improve query performance, pipeline reliability, and data processing efficiency.

Implement data quality checks, validation rules, reconciliation, and error-handling mechanisms.

Monitor production pipelines and troubleshoot data failures.

Apply data security, access controls, and governance standards.

Collaborate with data analysts, data scientists, architects, and business stakeholders.

Automate deployments and support CI/CD practices.

Maintain technical documentation and participate in Agile development activities.

Qualifications

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent experience.

5+ years of experience in data engineering, data integration, or ETL development.

Strong proficiency in Python, SQL, and data transformation techniques.

Hands-on experience with cloud data platforms and distributed processing.

Understanding of relational databases, data warehousing, and data lake architecture.

Experience with performance tuning, debugging, and production support

Required Technical Skills

Programming: Python, SQL, PySpark.

ETL/ELT: Informatica, Azure Data Factory, AWS Glue, or equivalent data integration tools.

Data Platforms: Snowflake, Databricks, Amazon Redshift, or Google BigQuery.

Big Data: Apache Spark, Hadoop, Kafka.

Databases: PostgreSQL, SQL Server, Oracle, or MySQL.

Cloud: AWS, Azure, or GCP.

Data Modeling: Star schema, snowflake schema, dimensional modeling, and data warehouse design.

DevOps: Git, CI/CD, Docker, and automated testing

Key Skills

Education

Bachelor’s degree