Senior Data Engineer -Databricks
Coimbatore
- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-Time
Employment type in the employer’s posting: Full-Time
From the employer’s posting
What you’ll work on
Full postingDesign and maintain ETL/ELT pipelines using Databricks, PySpark and SQL
Integrate data from databases, Amazon S3, files and REST APIs
Design Delta Lake tables using Bronze, Silver, Gold medallion architecture
Create, schedule, monitor and optimize Databricks Jobs and Workflows
Ensure data quality through validations, error handling, logging and monitoring
From the employer’s posting
Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, and SQL.
Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs.
Design and manage Delta Lake tables using Medallion Architecture (Bronze, Silver,Gold).
Create, schedule, monitor, and optimize Databricks Jobs and Workflows.
Ensure data quality through validations, error handling, logging, and monitoring.
What you’ll bring
All qualificationsPreferred experience
- 4+ years of experience in Data Engineering with 2+ years of hands-on Databricks experience.
Qualification wording
4+ years of experience in Data Engineering with 2+ years of hands-on Databricks experience. Data Governance experience would be an added advantage. ● Experience with Auto Loader, Spark Declarative pipelines, Kafka, Airflow, or dbt is a plus. ● Databricks certification is an added advantage.
Tools in this posting
- Python
- SQL
- AWS
- Databricks
- dbt
- Delta Lake
- Kafka
- S3
- Airflow
- PySpark
Source — Tool mentions in context
Position Overview We are looking for a Data Engineer with hands-on experience in building scalable data pipelines and data engineering solutions on the Databricks Lakehouse Platform. The ideal candidate should have strong expertise in Python, PySpark, SQL, Databricks, AWS, and REST API integrations for data ingestion, managing large volumes of data, and data export ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.
Basic Qualification: Strong expertise in Python, PySpark, and Advanced SQL. ● Hands-on experience with the Databricks Lakehouse Platform. ● Good understanding of Unity Catalog, Delta Lake, Databricks Workflows/Jobs, Clusters, Notebooks, Repos, and Medallion Architecture. ● Experience integrating with REST APIs for data ingestion and data export. ● Strong knowledge of ETL/ELT development, batch processing, incremental loading, and data transformation. Data Governance experience would be an added advantage. ● Experience with data modeling (Star Schema, Snowflake Schema, Fact & Dimension tables, SCD concepts). ● Understanding of data warehousing concepts and best practices. ● Experience working with structured and semi-structured data (CSV, JSON, Parquet, Delta). ● Knowledge of partitioning, file optimization, Spark performance tuning, and query optimization. ● Experience with Git and CI/CD best practices
Job Responsibilities: Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, and SQL. ● Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs. ● Build data pipelines with Databricks Unity Catalog. ● Implement business logic, data transformations, and dimensional data models. ● Create, schedule, monitor, and optimize Databricks Jobs and Workflows. ● Design and manage Delta Lake tables using Medallion Architecture (Bronze, Silver,Gold). ● Ensure data quality through validations, error handling, logging, and monitoring. ● Optimize Spark workloads for performance, scalability, and reliability. ● Collaborate with cross-functional teams to deliver production-ready data solutions. Basic Qualification:
Preferred Qualifications: 4+ years of experience in Data Engineering with 2+ years of hands-on Databricks experience. Data Governance experience would be an added advantage. ● Experience with Auto Loader, Spark Declarative pipelines, Kafka, Airflow, or dbt is a plus. ● Databricks certification is an added advantage. We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.
Posting record
Report an errorActively hiringFirst seen by us 12 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 17, 2026 · 12 days ago
- Recorded sightings
- 15
- Last seen by us
- Oct 29, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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Job description
Position Overview
We are looking for a Data Engineer with hands-on experience in building scalable data pipelines and data engineering solutions on the Databricks Lakehouse Platform. The ideal candidate should have strong expertise in Python, PySpark, SQL, Databricks, AWS, and REST API integrations for data ingestion, managing large volumes of data, and data export
ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation.
Job Responsibilities:
Design, develop, and maintain scalable ETL/ELT pipelines using Databricks,
PySpark, and SQL.
● Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs.
● Build data pipelines with Databricks Unity Catalog.
● Implement business logic, data transformations, and dimensional data models.
● Create, schedule, monitor, and optimize Databricks Jobs and Workflows.
● Design and manage Delta Lake tables using Medallion Architecture (Bronze, Silver,Gold).
● Ensure data quality through validations, error handling, logging, and monitoring.
● Optimize Spark workloads for performance, scalability, and reliability.
● Collaborate with cross-functional teams to deliver production-ready data solutions.
PySpark, and SQL.
● Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs.
● Build data pipelines with Databricks Unity Catalog.
● Implement business logic, data transformations, and dimensional data models.
● Create, schedule, monitor, and optimize Databricks Jobs and Workflows.
● Design and manage Delta Lake tables using Medallion Architecture (Bronze, Silver,Gold).
● Ensure data quality through validations, error handling, logging, and monitoring.
● Optimize Spark workloads for performance, scalability, and reliability.
● Collaborate with cross-functional teams to deliver production-ready data solutions.
Basic Qualification:
Strong expertise in Python, PySpark, and Advanced SQL.
● Hands-on experience with the Databricks Lakehouse Platform.
● Good understanding of Unity Catalog, Delta Lake, Databricks Workflows/Jobs,
Clusters, Notebooks, Repos, and Medallion Architecture.
● Experience integrating with REST APIs for data ingestion and data export.
● Strong knowledge of ETL/ELT development, batch processing, incremental loading, and data transformation.
● Hands-on experience with the Databricks Lakehouse Platform.
● Good understanding of Unity Catalog, Delta Lake, Databricks Workflows/Jobs,
Clusters, Notebooks, Repos, and Medallion Architecture.
● Experience integrating with REST APIs for data ingestion and data export.
● Strong knowledge of ETL/ELT development, batch processing, incremental loading, and data transformation.
Data Governance experience would be an added advantage.
● Experience with data modeling (Star Schema, Snowflake Schema, Fact & Dimension tables, SCD concepts).
● Understanding of data warehousing concepts and best practices.
● Experience working with structured and semi-structured data (CSV, JSON, Parquet, Delta).
● Knowledge of partitioning, file optimization, Spark performance tuning, and query optimization.
● Experience with Git and CI/CD best practices
● Experience with data modeling (Star Schema, Snowflake Schema, Fact & Dimension tables, SCD concepts).
● Understanding of data warehousing concepts and best practices.
● Experience working with structured and semi-structured data (CSV, JSON, Parquet, Delta).
● Knowledge of partitioning, file optimization, Spark performance tuning, and query optimization.
● Experience with Git and CI/CD best practices
Preferred Qualifications:
4+ years of experience in Data Engineering with 2+ years of hands-on Databricks
experience. Data Governance experience would be an added advantage.
● Experience with Auto Loader, Spark Declarative pipelines, Kafka, Airflow, or dbt is a plus.
● Databricks certification is an added advantage.
experience. Data Governance experience would be an added advantage.
● Experience with Auto Loader, Spark Declarative pipelines, Kafka, Airflow, or dbt is a plus.
● Databricks certification is an added advantage.
We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.
Employment type
Full-Time
Source details
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- Location & working pattern
Coimbatore
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