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Lead Data Engineering

3 months ago

Charlotte, United States Compunnel Inc. Full time

Interview Process:

  1. hacker rank (30mins SQL / 30 mins python)
  2. initial interview
  3. 1 hour technical
  4. 3 hour panel


Must Have Skills: "And if someone is coming from a software background and then getting into data engineering, that's a perfect fit."

  • Looking for around 12+ years of experience
  • Data - python & SQL are a must have
  • 2-3 years retail experience
  • Databricks (azure preferred)
  • Azure tech stack experience
  • experience building best practices and guidelines
  • knowledge of reusability patterns -python, data bricks
  • frameworks
  • should understand basics of data modeling
  • experience with facts & dimensions of how tables are built


Nice to have:

  • snowflake, fabric


Key Role details:

  • will be primarily developing in this role
  • this role doesn't touch production support (have a team offshore in india)
  • will be migrating SQL server to Azure
  • goal is to have use cases flow in 5 minutes
  • each lead will end up mentoring a team of 3-4 devs under them . They will be part of building out their own team.


Responsibilities:

  • Apply advanced knowledge of Data Engineering principles, methodologies and techniques to design and implement data loading and aggregation frameworks across broad areas of the organization.
  • Gather and process raw, structured, semi-structured and unstructured data using batch and real-time data processing frameworks.
  • Implement and optimize data solutions in enterprise data warehouses and big data repositories, focusing primarily on movement to the cloud.
  • Drive new and enhanced capabilities to Enterprise Data Platform partners to meet the needs of product / engineering / business.
  • Experience building enterprise systems especially using Databricks, Snowflake and platforms like Azure, AWS, GCP etc
  • Leverage strong Python, Spark, SQL programming skills to construct robust pipelines for efficient data processing and analysis.
  • Implement CI/CD pipelines for automating build, test, and deployment processes to accelerate the delivery of data solutions.
  • Implement data modeling techniques to design and optimize data schemas, ensuring data integrity and performance.
  • Drive continuous improvement initiatives to enhance performance, reliability, and scalability of our data infrastructure.
  • Collaborate with data scientists, analysts, and other stakeholders to understand business requirements and translate them into technical solutions.
  • Implement best practices for data governance, security, and compliance to ensure the integrity and confidentiality of our data assets.


Qualifications:

  • Bachelor’s or master’s degree in computer science, Engineering, or a related field.
  • Proven experience (8+) in a data engineering role, with expertise in designing and building data pipelines, ETL processes, and data warehouses.
  • Strong proficiency in SQL, Python and Spark programming languages.
  • Strong experience with cloud platforms such as AWS, Azure, or GCP is a must.
  • Hands-on experience with big data technologies such as Hadoop, Spark, Kafka, and distributed computing frameworks.
  • Knowledge of data lake and data warehouse solutions, including Databricks, Snowflake, Amazon Redshift, Google BigQuery, Azure Data Factory, Airflow etc.
  • Experience in implementing CI/CD pipelines for automating build, test, and deployment processes.
  • Solid understanding of data modeling concepts, data warehousing architectures, and data management best practices.
  • Excellent communication and leadership skills, with the ability to effectively collaborate with cross-functional teams and drive consensus on technical decisions.
  • Relevant certifications (e.g., Azure, databricks, snowflake) would be a plus.