Senior Data Engineer

Tanla Platforms Limited; Hyderabad; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

Tanla Platforms Limited is seeking a highly skilled Data Engineer to design and scale ETL/ELT pipelines for large-scale telecom datasets, enabling real-time analytics and BI across the platform ecosystem. You will build lakehouse solutions using Iceberg/Delta/Hudi, code in Python/Scala, and orchestration with Airflow or Kestra on AWS/GCP/Azure, collaborating with global engineering teams.

Strong SQL, data modeling, and cloud experience are required; telecom/CDR experience is a plus, with

Qualifications

• 7–10 years of experience in Data Engineering or related roles.
• Strong expertise in Advanced SQL including query optimization, partitioning, indexing, and performance tuning.
• Hands-on experience with Apache Spark (PySpark), Hadoop, Kafka.
• Expertise in lakehouse technologies such as Iceberg, Delta Lake, or Hudi.
• Experience with metadata/catalog systems including Nessie, Glue, or Hive Metastore.
• Knowledge of analytical engines such as ClickHouse, Dremio, Trino, or Presto.
• Strong understanding of Parquet, ORC, and Avro data formats.
• Experience with object storage systems like S3, ADLS, GCS, or Nutanix Object Storage.
• Proficient in Python / PySpark / Scala.
• Experience with Airflow, Kestra, or similar orchestration tools.
• Hands-on exposure to AWS, Azure, or GCP cloud platforms.
• Experience in telecom data systems or CDR processing is highly preferred.

Responsibilities

• Design and implement scalable ETL/ELT pipelines for analytics, data processing, and migrations.
• Build modern data lakehouse platforms using Iceberg, Delta Lake, or Hudi with catalog services.
• Develop and optimize high‑performance SQL queries and distributed processing using Spark, Hadoop, and Kafka.
• Design and manage data warehouses and analytical platforms using Snowflake, ClickHouse, Dremio, Redshift, Trino, or Presto.
• Build ingestion and transformation pipelines using object storage such as S3, ADLS, GCS, or Nutanix Storage.
• Process and transform telecom datasets and CDRs efficiently at scale.
• Implement orchestration workflows using Airflow, Kestra, or similar engines.
• Ensure data quality, governance, lineage, observability, scalability, and cost optimization across distributed systems.
• Build reusable frameworks for bulk data movement, ingestion acceleration, and transformation at scale.

Skills

ETL/ELT pipelines

Advanced SQL

Spark / PySpark

Hadoop

Kafka

Lakehouse tech

Python

Scala

Airflow

Kestra

Cloud Platforms

CDR experience

Tools

Iceberg

Delta Lake

Hudi

Nessie

Glue

Hive Metastore

Snowflake

ClickHouse

Dremio

Trino

Presto

Airflow

Kestra

S3

ADLS

GCS

AWS

Azure

GCP

Spark

Job description

We are looking for a highly skilled Data Engineer with strong experience in building scalable ETL/ELT pipelines, distributed data systems, and modern lakehouse architectures. The ideal candidate will work on large-scale telecom and CPaaS datasets, including Call Detail Records (CDR), enabling real-time analytics and business intelligence across the platform ecosystem.

What you’ll be Responsible for?

• Design and implement scalable ETL/ELT pipelines for large-scale analytics, data processing, and migration workloads.
• Build modern data lakehouse platforms using Iceberg, Delta Lake, or Hudi with catalog services like Nessie, AWS Glue, or Hive Metastore.
• Develop and optimize high-performance SQL queries and distributed data processing jobs using Spark (PySpark), Hadoop, and Kafka.
• Design and manage data warehouses and analytical platforms using Snowflake, ClickHouse, Dremio, Redshift, Trino, or Presto.
• Build ingestion and transformation pipelines using object storage systems such as Amazon S3, Azure Data Lake, GCS, or Nutanix Object Storage.
• Process and transform telecom datasets and Call Detail Records (CDR) efficiently at scale.
• Implement orchestration workflows using Airflow, Kestra, or similar workflow engines.
• Ensure data quality, governance, lineage, observability, scalability, and cost optimization across distributed systems.
• Build reusable frameworks for bulk data movement, ingestion acceleration, and transformation at scale.

What you’d have?

• 7 –10 years of experience in Data Engineering or related roles.
• Strong expertise in Advanced SQL including query optimization, partitioning, indexing, and performance tuning.
• Hands-on experience with Apache Spark (PySpark), Hadoop, Kafka, and distributed data processing systems.
• Strong expertise in lakehouse technologies such as Iceberg, Delta Lake, or Hudi.
• Experience with metadata/catalog systems including Nessie, Glue, or Hive Metastore.
• Knowledge of analytical engines such as ClickHouse, Dremio, Trino, or Presto.
• Strong understanding of Parquet, ORC, and Avro data formats.
• Experience with object storage systems like S3, ADLS, GCS, or Nutanix Object Storage.
• Strong programming skills in Python / PySpark / Scala.
• Experience with Airflow, Kestra, or similar orchestration tools.
• Hands-on exposure to AWS, Azure, or GCP cloud platforms.
• Experience in telecom data systems or CDR processing is highly preferred.
• Impactful Work: Build large-scale data platforms and analytics systems that power real-time communication products used by millions globally.
• Tremendous Growth Opportunities: Accelerate your career by solving complex engineering challenges in a fast-growing CPaaS and product-driven environment.
• Innovative Environment: Work alongside world-class engineers building cutting-edge distributed data systems, lakehouse architectures, and cloud-native platforms.

Tanla is an equal opportunity employer. We champion diversity and are committed to creating an inclusive environment for all employees.