Lead Data engineer
Job Title: Lead Data Engineer
Experience:
7–12 Years Location:
Bangalore Employment Type:
Full-Time Work Mode:
Work from Office ( Scala experience Must )
Job Summary We are looking for an experienced
Lead Data Engineer
to lead the design, development, and implementation of scalable data engineering solutions. The ideal candidate should have strong hands-on expertise in
Scala, Apache Spark, SQL, and cloud data platforms , along with experience in leading technical teams and delivering large-scale data pipelines. Key Responsibilities Lead the design and development of
high-volume, scalable data pipelines
and data processing frameworks. Develop robust
batch and real-time data processing solutions
using Apache Spark. Work extensively with
Scala, Spark, and SQL
to build and optimize data transformation pipelines. Design and implement data solutions on
cloud platforms such as AWS, Azure, or GCP . Develop and maintain data pipelines for structured and unstructured data. Perform performance tuning and optimization of
Spark jobs, SQL queries, and data pipelines . Work with technologies such as
Kafka
for real-time/streaming data processing. Ensure data quality, reliability, security, and governance across data platforms. Provide technical leadership, conduct code reviews, and mentor junior and mid-level engineers. Mandatory Skills 7–12 years of experience
in Data Engineering. Strong hands-on experience with
Scala + Apache Spark . Excellent knowledge of
SQL
and database concepts. Strong experience building
ETL/ELT data pipelines . Experience with
Spark Batch and Spark Streaming . Experience with
Kafka
or other streaming technologies. Hands-on experience with at least one
cloud platform – AWS / Azure / GCP . Strong understanding of
data warehousing, data lakes, and distributed data processing . Experience with performance tuning and optimization of Spark applications. Good understanding of
CI/CD, Git, and Agile methodologies . Strong problem-solving and communication skills. Good to Have Experience with
Databricks / Delta Lake . Knowledge of
Python/PySpark . Experience with
Airflow
or other workflow orchestration tools. Experience with
Docker/Kubernetes . Knowledge of modern
Lakehouse architecture . Experience leading a team or mentoring data engineers.
7–12 Years Location:
Bangalore Employment Type:
Full-Time Work Mode:
Work from Office ( Scala experience Must )
Job Summary We are looking for an experienced
Lead Data Engineer
to lead the design, development, and implementation of scalable data engineering solutions. The ideal candidate should have strong hands-on expertise in
Scala, Apache Spark, SQL, and cloud data platforms , along with experience in leading technical teams and delivering large-scale data pipelines. Key Responsibilities Lead the design and development of
high-volume, scalable data pipelines
and data processing frameworks. Develop robust
batch and real-time data processing solutions
using Apache Spark. Work extensively with
Scala, Spark, and SQL
to build and optimize data transformation pipelines. Design and implement data solutions on
cloud platforms such as AWS, Azure, or GCP . Develop and maintain data pipelines for structured and unstructured data. Perform performance tuning and optimization of
Spark jobs, SQL queries, and data pipelines . Work with technologies such as
Kafka
for real-time/streaming data processing. Ensure data quality, reliability, security, and governance across data platforms. Provide technical leadership, conduct code reviews, and mentor junior and mid-level engineers. Mandatory Skills 7–12 years of experience
in Data Engineering. Strong hands-on experience with
Scala + Apache Spark . Excellent knowledge of
SQL
and database concepts. Strong experience building
ETL/ELT data pipelines . Experience with
Spark Batch and Spark Streaming . Experience with
Kafka
or other streaming technologies. Hands-on experience with at least one
cloud platform – AWS / Azure / GCP . Strong understanding of
data warehousing, data lakes, and distributed data processing . Experience with performance tuning and optimization of Spark applications. Good understanding of
CI/CD, Git, and Agile methodologies . Strong problem-solving and communication skills. Good to Have Experience with
Databricks / Delta Lake . Knowledge of
Python/PySpark . Experience with
Airflow
or other workflow orchestration tools. Experience with
Docker/Kubernetes . Knowledge of modern
Lakehouse architecture . Experience leading a team or mentoring data engineers.