Senior Data Engineer

• Build, optimize, and maintain ETL pipelines using Hadoop ecosystem tools (HDFS, Hive, Spark)
• Assemble, process, and manage large, complex datasets to support analytics, BI, and AI-driven applications
• Collaborate with Software Engineers, Data Scientists, and Architects to deliver efficient, reliable, and scalable data processing solutions
• Perform data modelling, quality checks, and system performance tuning to ensure accuracy and efficiency
• Design and implement process improvements for automation, workflow orchestration, and data scalability
• Support modernization efforts, including cloud adoption and Databricks integration
• Take ownership of clarifying requirements and proposing scalable, secure solutions before implementation

• Strong experience with SQL and relational/NoSQL databases (Postgres, Oracle, CosmosDB)
• Hands-on expertise in big data frameworks such as Spark, Hadoop, Hive, Impala, Oozie, Airflow, and HDFS
• Proficiency in Java, Scala, or Python for data engineering and automation
• Solid understanding of distributed data processing, stream processing, and message queuing (Kafka, Spark Streaming, Storm)
• Cloud experience (preferably AWS) with services such as S3, Athena, EMR, Redshift, Glue, Lambda
• Experience with Snowflake or similar data warehousing solutions
• Familiarity with Databricks and AI/ML integration for data platforms
• CI/CD, containerization (Docker), and workflow management experience
• Comfortable working in Agile environments (Scrum, SAFe) and collaborating across cross-functional teams
• Strong problem-solving skills and ability to extract value from large, complex datasets

Azure or GCP experience a plus