ShyftLabs

Data-driven decision-making platform for organizations

Technical Data Engineer Databricks

Data EngineeringPosted on Nov 10

Full-timeCoimbatore, Tamil Nadu, IndiaNo salary listedHybrid

About the Job

Requirements

Strong expertise in Python, PySpark, and advanced SQL.
Hands-on experience with the Databricks Lakehouse Platform.
A good understanding of Unity Catalog, Delta Lake, Databricks Workflows and Jobs, clusters, notebooks, repos, and Medallion Architecture.
Experience integrating REST APIs for data ingestion and data export.
Strong knowledge of ETL/ELT development, batch processing, incremental loading, and data transformation.
Experience with data modeling, including Star Schema, Snowflake Schema, fact and dimension tables, and slowly changing dimension concepts.
Understanding of data warehousing concepts and best practices.
Experience working with structured and semi-structured data, including CSV, JSON, Parquet, and Delta.
Knowledge of partitioning, file optimization, Spark performance tuning, and query optimization.
Experience with Git and CI/CD best practices.

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, and 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.

Desired Qualifications

Four or more years of experience in data engineering, including two or more years of hands-on Databricks experience.
Data Governance experience.
Experience with Auto Loader, Spark Declarative Pipelines, Kafka, Airflow, or dbt.
Databricks certification.

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.

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

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.

About the Company

ShyftLabs

Data-driven decision-making platform for organizations

ShyftLabs helps organizations adopt a data-first approach to decision making by establishing repeatable data practices and governance that turn data into timely insights. Its offering designs and implements data-driven decision workflows and analytics pipelines so teams access trustworthy data with clearly defined metrics and actions. Unlike tools that provide dashboards alone, ShyftLabs builds end-to-end processes that standardize how data is measured, analyzed, and used in decisions. Its goal is to help organizations move faster and make better choices, staying ahead of the competition.

Company size: 11-50
Company stage: Not listed
Total funding: Not listed
Headquarters: Canada
Founded: 2018

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