Key Responsibilities:

Design and Development :

Design, develop, and maintain scalable ETL pipelines using cloud-native tools (AWS DMS, AWS Glue, Kafka, Azure Data Factory, GCP Dataflow, etc.).
Architect and implement data lakes and data warehouses on cloud platforms (AWS, Azure, GCP).
Develop and optimize data ingestion, transformation, and loading processes using Databricks, Snowflake, Redshift, BigQuery and Azure Synapse.
Implement ETL processes using tools like Informatica, SAP Data Intelligence, and others.
Develop and optimize data processing jobs using Spark Scala.

Data Integration and Management :

Integrate various data sources, including relational databases, APIs, unstructured data, and ERP systems into the data lake.
Ensure data quality and integrity through rigorous testing and validation.
Perform data extraction from SAP or ERP systems when necessary.

Performance Optimization:

Monitor and optimize the performance of data pipelines and ETL processes.
Implement best practices for data management, including data governance, security, and compliance.

Collaboration and Communication :

Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions.
Collaborate with cross-functional teams to design and implement data solutions that meet business needs.

Documentation and Maintenance:

Education:

Bachelor s degree in Computer Science, Information Technology, or a related field. Advanced degrees are a plus.

Experience:

7+ years of experience as a Data Engineer or in a similar role.
Proven experience with cloud platforms: AWS, Azure, and GCP.
Hands-on experience with cloud-native ETL tools such as AWS DMS, AWS Glue, Kafka, Azure Data Factory, GCP Dataflow, etc.
Experience with other ETL tools like Informatica, SAP Data Intelligence, etc.
Experience in building and managing data lakes and data warehouses.
Proficiency with data platforms like Redshift, Snowflake, BigQuery, Databricks, and Azure Synapse.
Experience with data extraction from SAP or ERP systems is a plus.
Strong experience with Spark and Scala for data processing.

Skills:

Strong programming skills in Python, Java, or Scala.
Proficient in SQL and query optimization techniques.
Familiarity with data modeling, ETL/ELT processes, and data warehousing concepts.
Knowledge of data governance, security, and compliance best practices.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills.

Preferred Qualifications:

Experience with other data tools and technologies such as Apache Spark, or Hadoop.
Certifications in cloud platforms (AWS Certified Data Analytics - Specialty, Google Professional Data Engineer, Microsoft Certified: Azure Data Engineer Associate).
Experience with CI/CD pipelines and DevOps practices for data engineering
Selected applicant will be subject to a background investigation, which will be conducted and the results of which will be used in compliance with applicable law.

AWS Databricks Data Engineer

Cloud Data Engineer - Databricks on AWS/Scala