Senior Data Engineer - Python & Pyspark

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Job Overview

• Data Architecture & Design:Design, develop, and optimize data architectures, pipelines, and data models to support various business needs, including analytics, reporting, and machine learning.
• ETL/ELT Development (Python/PySpark Focus): Build, test, and deploy highly scalable and efficient ETL/ELT processes usingPython and PySpark to ingest, transform, and load data from diverse sources into data warehouses and data lakes. Develop and optimize complex data transformations using PySpark.
• Data Quality & Governance:Implement best practices for data quality, data governance, and data security to ensure the integrity, reliability, and privacy of our data assets.
• Performance Optimization:Monitor, troubleshoot, and optimize data pipeline performance, ensuring data availability and timely delivery, particularly for PySpark jobs.
• Infrastructure Management:Collaborate with DevOps and MLOps teams to manage and optimize data infrastructure, including cloud resources (AWS, Azure, GCP), databases, and data processing frameworks, ensuring efficient operation of PySpark clusters.
• Mentorship & Leadership:Provide technical guidance, mentorship, and code reviews to junior data engineers, particularly in Python and PySpark best practices, fostering a culture of excellence and continuous improvement.
• Collaboration: Work closely with data scientists, analysts, product managers, and other stakeholders to understand data requirements and deliver solutions that meet business objectives.
• Innovation:Research and evaluate new data technologies, tools, and methodologies to enhance our data capabilities and stay ahead of industry trends.
• Documentation: Create and maintain comprehensive documentation for data pipelines, data models, and data infrastructure.

• Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related quantitative field.

• 5+ years of professional experience in data engineering, with a strong emphasis on building and maintaining large-scale data systems.
• Extensive hands-on experience with Python for data engineering tasks.
• Proven experience with PySpark for big data processing and transformation.
• Proven experience with cloud data platforms (e.g., AWS Redshift, S3, EMR, Glue; Azure Data Lake, Databricks, Synapse; Google BigQuery, Dataflow).
• Strong experience with SQL and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Cassandra).
• Extensive experience with distributed data processing frameworks, especially Apache Spark.

• Programming Languages: Expert proficiency inPython is mandatory. Strong SQL mastery is essential. Familiarity with Scala or Java is a plus.
• Big Data Technologies: In-depth knowledge and hands-on experience withApache Spark (PySpark) for data processing, including Spark SQL, Spark Streaming, and DataFrame API. Experience with Apache Kafka, Apache Airflow, Delta Lake, or similar technologies.
• Data Warehousing: In-depth knowledge of data warehousing concepts, dimensional modeling, and ETL/ELT processes.
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, GCP) and their data services, particularly those supporting Spark/PySpark workloads.
• Containerization: Familiarity with Docker and Kubernetes is a plus.
• Version Control: Proficient with Git and CI/CD pipelines.

• Excellent problem-solving and analytical abilities.
• Strong communication and interpersonal skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• Ability to work effectively in a fast-paced, agile environment.
• Proactive and self-motivated with a strong sense of ownership.

• Experience with real-time data streaming and processing using PySpark Structured Streaming.
• Knowledge of machine learning concepts and MLOps practices, especially integrating ML workflows with PySpark.
• Familiarity with data visualization tools (e.g., Tableau, Power BI).
• Contributions to open-source data projects.