What You’ll Do
- Architect and implement scalable, secure, and reliable data pipelines using modern data platforms (e.g., Spark, Databricks, Airflow, Snowflake, etc.).
- Develop ETL/ELT processes to ingest data from various structured and unstructured sources.
- Perform Exploratory Data Analysis (EDA) to uncover trends, validate data integrity, and derive insights that inform data product development and business decisions.
- Collaborate closely with data scientists, analysts, and software engineers to design data models that support high-quality analytics and real-time insights.
- Lead data infrastructure projects including management of data on cloud platforms (AWS/Azure), data lake/warehouse implementations, and data quality frameworks.
- Ensure data governance, security, and compliance best practices are followed.
- Monitor and optimize the performance of data systems, addressing any issues proactively.
- Mentor junior data engineers and contribute to establishing best practices in data engineering standards, tooling, and development workflows.
- Stay current with emerging technologies and trends in data engineering and recommend improvements as needed.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- 8+ years of hands-on experience in data engineering or backend software development roles.
- Proficiency with Python, SQL, and at least one data pipeline orchestration tool (e.g., Apache Airflow, Luigi, Prefect).
- Strong experience with cloud-based data platforms (e.g., AWS Redshift, GCP BigQuery, Snowflake, Databricks).
- Deep understanding of data modeling, data warehousing, and distributed systems.
- Experience with big data technologies such as Apache Spark, Kafka, Hadoop, etc.
- Familiarity with DevOps practices (CI/CD, infrastructure as code, containerization with Docker/Kubernetes).
Preferred Qualifications
- Experience working with real-time data processing and streaming data architectures.
- Knowledge of data security and privacy regulations (e.g., GDPR, HIPAA).
- Exposure to machine learning pipelines or supporting data science workflows.
- Familiarity with prompt engineering and how LLM-based systems interact with data.
- Experience working in cross-functional teams and with stakeholders from non-technical domains.

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