Technical Specialist
Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Oct 30, 2026.
Job Summary
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 PySpark/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
Ideal 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, NiFi), 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 NiFi, or Talend.
Working knowledge of DevOps/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.
Key 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 PySpark/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
Ideal 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, NiFi), 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 NiFi, or Talend.
Working knowledge of DevOps/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.
Skill Requirements
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 PySpark/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
Ideal 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, NiFi), 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 NiFi, or Talend.
Working knowledge of DevOps/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.
Other Requirements
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 PySpark/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
Ideal 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, NiFi), 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 NiFi, or Talend.
Working knowledge of DevOps/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.