Data Engineer

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.

LONGDESCRIPTION section. 2 of 6.

Section Title: Key Responsibilities

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.

LONGDESCRIPTION section. 3 of 6.

Section Title: Skill Requirements

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.

SKILL section. 4 of 6.

Section Title: Must Have Skills

Must Have Skills

• Click Enter to show the proficiency description of Data EngineeringData Engineering
• Click Enter to show the proficiency description of DatabricksDatabricks
• Click Enter to show the proficiency description of Apache SparkApache Spark
• Click Enter to show the proficiency description of PySparkPySpark
• Click Enter to show the proficiency description of PythonPython
• Click Enter to show the proficiency description of CI/CDCI/CD

SKILL section. 5 of 6.

Section Title: Good to have Skills

Good to have Skills

LONGDESCRIPTION section. 6 of 6.

Section Title: Other Requirements

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