Roles and responsibilities
- Design, develop, and optimize data pipelines and ETL processes for efficient data ingestion, transformation, and integration from multiple sources.
- Manage and maintain data warehouses and data lakes, ensuring accuracy, availability, and security.
- Work closely with business stakeholders, data analysts, and data scientists to translate business needs into technical solutions.
- Implement data quality frameworks, monitoring systems, and performance tuning.
- Contribute to data modeling, architecture, and governance best practices.
- Support the development of dashboards, reports, and analytical models to provide actionable insights.
- Mentor junior engineers and foster a culture of collaboration and technical excellence within the data team.
Skills and Qualifications
- Strong experience in SQL, Python. Familiarity with data warehousing technologies (e.g., Snowflake, Redshift, BigQuery) and ETL tools (e.g., Apache Airflow, Talend, Fivetran).
- Experience with data visualization tools such as Tableau, Power BI, or Looker.
- Knowledge of cloud-based platforms (AWS, Google Cloud, Azure) and associated data services.
- Experience with Hadoop, Spark, Kafka, or other big data technologies is a plus.
- Strong understanding of relational databases, data modeling concepts, and designing data architectures.
- Ability to analyze complex data sets and identify trends, correlations, and insights.
- Excellent verbal and written communication skills, with the ability to explain complex data concepts to non-technical stakeholders.
Experiences
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (Master’s preferred).
- 2+ years of experience in data engineering or a related field, preferably in analytics or business intelligence.
- Strong experience working with large datasets and complex data pipelines.
- Previous experience in a similar industry or field is a plus.