Data Engineer

Key Responsibilities

Design, develop, and maintain robust ETL/ELT pipelines using SQL, dbt, and orchestration tools.

Architect and implement data warehouse and lakehouse solutions leveraging Snowflake, Databricks, and cloud-native services.

Work on data modeling, schema design, and query optimization for performance and scalability.

Migrate and integrate legacy/on-prem data platforms to cloud environments (Azure or AWS).

Implement data governance, quality, and security practices to ensure reliability and compliance.

Collaborate with data scientists, BI developers, and business stakeholders to deliver analytics-ready datasets.

Optimize pipelines and storage for cost efficiency and cloud-native performance.

Stay current with best practices and emerging tools in data engineering and cloud ecosystems.

Required Qualifications

Bachelors degree in Computer Science, Data Engineering, Information Systems, or related field (or equivalent experience).

5+ years of experience in Data Engineering or related roles.

Solid foundation in SQL, relational databases, and data architecture principles.

Hands-on experience with Snowflake, Databricks, and dbt.

Experience integrating data platforms into cloud environments (Azure or AWS).

Strong understanding of data modeling, warehousing, and ELT/ETL processes.

Proficiency in Python or Scala for data transformation and automation.

Familiarity with version control (Git), CI/CD pipelines, and orchestration tools (Airflow, Prefect, etc.).

Preferred Skills

Experience with real-time/streaming data (Kafka, Kinesis, Event Hub). Exposure to Azure Synapse Analytics, AWS Redshift, or similar platforms.

Knowledge of Terraform, CloudFormation, or Infrastructure-as-Code practices.

Familiarity with BI tools such as Power BI, Tableau, or Looker.

Strong problem-solving, analytical, and communication skills.