Posted 9 days ago
Data Engineer
@ HCLTech
Vancouver, British Columbia
Overview
HCLTech is hiring a full-time Data Engineer to build and run large-scale data pipelines on big data platforms. The role centres on ETL/ELT work on Hadoop and Databricks, with performance tuning and CI/CD for data workflows.
Responsibilities:
- Build and maintain ETL/ELT pipelines on Hadoop and enterprise data platforms
- Develop PySpark and Python data processing jobs on Cloudera and Databricks
- Optimize pipeline performance and cost through partitioning and compute tuning
- Contribute to CI/CD for data workflows and code reviews
- Partner with Product Managers to scope new data features
Requirements summary:
- Hands-on production data engineering on Hadoop and Databricks
- Strong Python, Spark and SQL skills for big data products
- Experience with Airflow, NiFi or Talend orchestration
- Working knowledge of Git, automated testing and release pipelines
- Strong debugging and stakeholder communication skills
Tools & skills
Source and dates
Source type not verified · apify-linkedin
- Employer posting date
- 2026-11-01
- First seen by our source
- 2026-11-11
- Source record updated
- 2026-11-11
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Department | Software development |
| Hours per week | 40 (estimated) |
| Work setting | Office |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
Summary and details written by AI from the posting; the employer’s posting is the authority.
HCLTech
| Field | Value |
|---|---|
| Open jobs on Jobsy | 18 |
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
Journal
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