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Cloud Data Engineering Lead

We are seeking an experienced Cloud Data Engineering Lead to design and implement scalable, high-performance data pipelines.

The role involves building and optimizing ETL/ELT workflows, managing big data environments, and ensuring seamless data availability for analytics, AI/ML, and business intelligence use cases.

Key Responsibilities:
  • Design, develop, and maintain data pipelines and workflows using AWS (S3, Redshift, Glue, EMR, Lambda, Kinesis, Athena) and Databricks.
  • Build and optimize ETL/ELT processes for large-scale structured and unstructured datasets.
  • Leverage Databricks for data engineering, advanced analytics, and machine learning workloads.
  • Collaborate with data scientists, analysts, and stakeholders to deliver reliable and scalable data solutions.
  • Implement data governance, quality checks, and monitoring frameworks.
  • Ensure high availability, scalability, and cost-effectiveness of data solutions.
Required Skills:
  • 7+ years of experience in data engineering.
  • Strong hands-on experience with AWS cloud services (S3, Redshift, Glue, EMR, Athena, Lambda, Kinesis).
  • Expertise in Databricks (PySpark, Delta Lake, ML pipelines).
  • Proficiency in Python and SQL for data transformation and querying.
  • Strong understanding of data warehousing, data modeling, and big data frameworks.
  • Experience with workflow orchestration tools (Airflow, Step Functions).
Preferred Qualifications:
  • Familiarity with MLOps/ML integration within Databricks.
  • Experience with real-time/streaming data (Kafka, Spark Streaming).
  • Knowledge of infrastructure as code (Terraform/CloudFormation).
  • Exposure to data security, compliance, and governance practices.

Our ideal candidate will have a strong track record of delivering complex data engineering projects on time and on budget. They will be able to effectively communicate technical concepts to both technical and non-technical stakeholders and possess excellent problem-solving skills.