* Managing Consultant Data Analytics

We are looking for a skilled Data Engineer to design, develop, and maintain scalable data pipelines and processing solutions that support analytics and business intelligence initiatives. The ideal candidate will have extensive hands-on experience with SQL, Python, ETL/ELT development, data warehousing, and cloud-based platforms. This role requires building reliable, efficient data solutions that handle both structured and unstructured data across various environments.

Key Responsibilities

- Design, develop, and maintain scalable ETL/ELT data pipelines for data extraction, transformation, and loading.
- Develop and optimize complex SQL queries, stored procedures, and improve overall database performance.
- Build and manage data integration workflows to process data from multiple sources.
- Work with cloud data platforms such as Azure, AWS, or Google Cloud to deploy and manage data solutions.
- Develop data processing applications using Python, PySpark, or similar technologies.
- Design, implement, and maintain data warehouses, data lakes, and lakehouse architectures.
- Implement data quality checks, validation processes, monitoring systems, and error-handling mechanisms.
- Optimize data pipelines for performance, scalability, reliability, and cost efficiency.
- Collaborate closely with data scientists, analysts, BI developers, and business stakeholders to understand and fulfill data requirements.
- Utilize orchestration tools like Apache Airflow, Azure Data Factory, or equivalent platforms to automate workflows.
- Ensure data security, governance, access controls, and compliance with relevant standards.
- Prepare technical documentation and provide support during production deployments and troubleshooting.

Required Qualifications

- Minimum of 5 years of experience in data engineering, ETL development, or a related field.
- Strong proficiency in SQL and Python for data processing and transformation tasks.
- Hands-on experience with ETL/ELT tools and building data pipelines.
- Solid experience with data warehousing, data modeling, and relational database technologies such as SQL Server, PostgreSQL, MySQL, or Oracle.
- Experience working with cloud data platforms including Azure, AWS, or Google Cloud.
- Knowledge of big data technologies such as Apache Spark, PySpark, Hadoop, or Kafka.
- Familiarity with orchestration and integration tools like Azure Data Factory, Apache Airflow, Informatica, or similar.
- Understanding of data lake, data warehouse, and lakehouse architectural concepts.
- Proven ability to implement data quality, validation, monitoring, and optimize pipeline performance.
- Strong analytical thinking, problem-solving abilities, and excellent communication and collaboration skills.

Preferred Qualifications and Benefits

- Experience with platforms such as Databricks, Snowflake, Microsoft Fabric, or dbt is highly desirable.
- Relevant cloud or data engineering certifications are considered a plus.

This position offers the opportunity to work on cutting-edge data infrastructure projects within a collaborative environment, supporting critical business intelligence and analytics functions. If you have a passion for building scalable data solutions and thrive in a fast-paced, cloud-focused setting, we encourage you to apply.