Overview We are looking for a Senior Data Engineer who will help in implementing data analytics products using on-prem and cloud data platforms.
Position Responsibilities Work with cutting-edge big data platforms (e.g., Databricks, Apache Spark) at large scale, pushing the boundaries of data processing and model enablement.
Build and maintain robust ETL/ELT pipelines for ingestion, transformation, and aggregation of large-scale datasets on Hadoop and enterprise data platforms.
Develop high-performance data processing jobs using Py Spark/Spark, Python on data platforms such as cloudera and databricks.
Optimize pipeline performance and cost through partitioning, file formats, compute tuning, and efficient query patterns Contribute to CI/CD for data workflows (testing, code reviews, deployment automation), promoting engineering best practices and maintainable codebases.




Partner with Product Managers to develop a deep understanding of users and use cases and apply that knowledge to scoping and building new modules and features Adecuado Candidate Qualifications Strong hands-on experience in data engineering building production-grade pipelines on big data platforms (Hadoop ecosystem and cloud data platforms - databricks).
High proficiency in using Python, Spark, Hadoop platforms & tools (Hive, Impala, Airflow, Ni Fi), SQL to build Big Data products.
Hands-on experience with cloud data platforms such as databricks, snowflake (databricks preferred) Experience with orchestration/integration tools such as Apache Airflow, Apache Ni Fi, or Talend.
Working knowledge of Dev Ops/CI-CD practices: version control (Git), automated testing, release pipelines, and observability.
Strong problem-solving skills with the ability to debug complex data issues and communicate clearly with technical and non-technical stakeholders.
Experience developing Java based applications is an added advantage.
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