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We are seeking a skilled Cloud Data Engineer who has experience with cloud data platforms like AWS or Azure and especially Snowflake and dbt to join our dynamic team. As a consultant, you will be responsible for developing new data platforms and create the data processes. You will collaborate with cross-functional teams to design, develop, and deploy high-quality frontend solutions.
Responsibilities:
Customer consulting: You develop data-driven products in the Snowflake Cloud and connect data & analytics with specialist departments. You develop ELT processes using dbt (data build tool)
Specifying requirements: You develop concrete requirements for future-proof cloud data architectures.
Develop data routes: You design scalable and powerful data management processes.
Analyze data: You derive sound findings from data sets and present them in an understandable way.
Requirements:
Requirements management and project experience: You successfully implement cloud-based data & analytics projects.
Data architectures: You are proficient in DWH/data lake concepts and modeling with Data Vault 2.0.
Cloud expertise: You have extensive knowledge of Snowflake, dbt and other cloud technologies (e.g. MS Azure, AWS, GCP).
SQL know-how: You have a sound and solid knowledge of SQL.
Data management: You are familiar with topics such as master data management and data quality.
Bachelor's degree in computer science, or a related field.
Strong communication and collaboration abilities to work effectively in a team environment.
Skills & Requirements
Cloud Data Engineering, AWS, Azure, Snowflake, dbt, ELT processes, Data-driven consulting, Cloud data architectures, Scalable data management, Data analysis, Requirements management, Data warehousing, Data lake, Data Vault 2.0, SQL, Master data management, Data quality, GCP, Strong communication, Collaboration.
• Key Skillset: -
• Advanced SQL Skills and good Communication Skills are mandatory.
• Develop and execute detailed Data warehouse related functional, integration and regression test cases, and documentation
• Prioritize testing tasks based on goals and risks of projects and ensure testing milestones, activities and tasks are completed as scheduled.
• Develop and Design Datawarehouse testcases, scenarios, and scripts to ensure quality Data warehouse /BI applications.
• Report the status of test planning, defects and execution activities, including regular status updates to the project team.
• Hands on Experience on any SQL Too

We are looking for an outstanding Big Data Engineer with experience setting up and maintaining Data Warehouse and Data Lakes for an Organization. This role would closely collaborate with the Data Science team and assist the team build and deploy machine learning and deep learning models on big data analytics platforms.
Roles and Responsibilities:
- Develop and maintain scalable data pipelines and build out new integrations and processes required for optimal extraction, transformation, and loading of data from a wide variety of data sources using 'Big Data' technologies.
- Develop programs in Scala and Python as part of data cleaning and processing.
- Assemble large, complex data sets that meet functional / non-functional business requirements and fostering data-driven decision making across the organization.
- Responsible to design and develop distributed, high volume, high velocity multi-threaded event processing systems.
- Implement processes and systems to validate data, monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it.
- Perform root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
- Provide high operational excellence guaranteeing high availability and platform stability.
- Closely collaborate with the Data Science team and assist the team build and deploy machine learning and deep learning models on big data analytics platforms.
Skills:
- Experience with Big Data pipeline, Big Data analytics, Data warehousing.
- Experience with SQL/No-SQL, schema design and dimensional data modeling.
- Strong understanding of Hadoop Architecture, HDFS ecosystem and eexperience with Big Data technology stack such as HBase, Hadoop, Hive, MapReduce.
- Experience in designing systems that process structured as well as unstructured data at large scale.
- Experience in AWS/Spark/Java/Scala/Python development.
- Should have Strong skills in PySpark (Python & SPARK). Ability to create, manage and manipulate Spark Dataframes. Expertise in Spark query tuning and performance optimization.
- Experience in developing efficient software code/frameworks for multiple use cases leveraging Python and big data technologies.
- Prior exposure to streaming data sources such as Kafka.
- Should have knowledge on Shell Scripting and Python scripting.
- High proficiency in database skills (e.g., Complex SQL), for data preparation, cleaning, and data wrangling/munging, with the ability to write advanced queries and create stored procedures.
- Experience with NoSQL databases such as Cassandra / MongoDB.
- Solid experience in all phases of Software Development Lifecycle - plan, design, develop, test, release, maintain and support, decommission.
- Experience with DevOps tools (GitHub, Travis CI, and JIRA) and methodologies (Lean, Agile, Scrum, Test Driven Development).
- Experience building and deploying applications on on-premise and cloud-based infrastructure.
- Having a good understanding of machine learning landscape and concepts.
Qualifications and Experience:
Engineering and post graduate candidates, preferably in Computer Science, from premier institutions with proven work experience as a Big Data Engineer or a similar role for 3-5 years.
Certifications:
Good to have at least one of the Certifications listed here:
AZ 900 - Azure Fundamentals
DP 200, DP 201, DP 203, AZ 204 - Data Engineering
AZ 400 - Devops Certification