Designs, builds, and maintains scalable data pipelines, ETL processes, data architectures, and integrations. Ensures data quality, governance, security, compliance, and accessibility while monitoring performance and troubleshooting issues. Collaborates with data scientists, analysts, and engineers to define requirements and deliver reliable solutions. Participates in code reviews, documentation, data modeling, schema design, and CI/CD implementation for data workflows.
We
are seeking a detail-oriented and highly motivated Data Engineer to join our
growing Data & Analytics team.
In this role, you will be responsible for designing, building, and maintaining
robust data pipelines and infrastructure that power insights across the
organization. You’ll work closely with data scientists, analysts, and engineers
to ensure the integrity, accessibility, and scalability of our data systems.
Key
Responsibilities:
- Design, develop, and maintain scalable
data pipelines and ETL processes.
- Build and optimize data architecture to
ensure data quality and consistency.
- Integrate data from diverse internal
and external sources.
- Collaborate with cross-functional teams
to define data requirements and deliver solutions.
- Implement best practices for data
governance, security, and compliance.
- Monitor pipeline performance and
perform real-time troubleshooting of data issues.
- Participate in code reviews and
contribute to documentation and standards.
Required
Qualifications & Skills:
- Bachelor’s degree in computer science,
Engineering, or a related field (or equivalent practical experience).
- 5+ years of professional experience in
data engineering.
- Solid understanding of SQL and
proficiency in at least one programming language, such as Python, Java, or
Scala.
- Practical experience building and
maintaining data pipelines using tools like Apache Airflow or DBT.
- Hands-on experience with cloud
platforms (AWS, GCP, Azure) and data warehousing solutions (Redshift, BigQuery,
Snowflake).
- Familiarity with big data technologies
and frameworks, including Spark, Kafka, and Hadoop.
- Demonstrated ability to solve complex
problems with a strong focus on detail.
- Experience implementing CI/CD practices
for data workflows.
- Working knowledge of data modelling
principles and schema design.
- Exposure to machine learning pipelines
or real-time analytics systems is a plus
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