Job Description
Job Title
Senior Data Engineer
Department
R&D
Position Type
Full-time
Job Location
Bangalore
Shift Timings
Regular
Work Mode
Hybrid
Industry Type
Telecom
Travel Required
NA
Why Us?
MACH is an innovator and leader in global monetization solutions. Their software optimizes and orchestrates critical inter-carrier business processes including data and financial clearing, network roaming, real-time campaign management, and interconnect solutions. MACH creates transformative communications solutions for today, tomorrow, and beyond, bridging wholesale and inter-carrier connectivity with roaming and clearing.
Collaboration and Innovation are at MACH’s core. Our success is built on diverse strengths across different teams, functions, and geographies summarised in our company values:
- Open collaboration
- Driving innovation
- Customer commitment
- Growth mindset
- Deliver results
Please visit our website to know more about us - https://mach.com/
A Day in the Life of Senior Data Engineer:
We are seeking a highly skilled and analytical Senior Data Engineer with deep expertise in designing, building, and maintaining end-to-end distributed data pipelines within the Apache Hadoop ecosystem. In this role,
- You will lead the architecture and implementation of scalable batch and real-time streaming pipelines.
- You will be responsible for ingest, storage, large-scale processing, and serving layers, transforming massive volumes of unstructured, semi-structured, and structured data into high-performance datasets ready for downstream analytics and machine learning applications
So, how are we going to keep you busy?
- Pipeline Architecture: Design, build, and optimize scalable end-to-end data pipelines (ETL/ELT) using Hadoop ecosystem components.
- Data Ingestion: Implement high-throughput ingestion pipelines utilizing Apache Nifi or Apache Kafka or Flume to capture streaming event data and transactional batch files.
- Distributed Processing: Develop robust data transformation workflows using Apache Spark (Core, SparkSQL, Structured Streaming) and MapReduce.
- Data Storage & Modeling: Optimize data layout in HDFS utilizing columnar formats (Parquet, ORC) and manage NoSQL persistence layers using HBase or Cassandra
- Query Performance Optimization: Author and tune highly complex distributed queries in Apache Hive to meet tight SLAs.
- Orchestration & Workflow Management: Automate and monitor multi-stage pipeline DAGs using orchestration tools like Apache Airflow or Oozie.
- Cluster Performance Tuning: Partner with DevOps/Infrastructure teams to debug issues related to data skew, resource allocation (YARN), cluster memory management, and Spark join strategies.
What will make you eligible for this role?
- Relevant bachelor’s degree or master’s degree
- 6-8 years of relevant experience in IT industry (Telecom preferred but not mandatory).
- 3 to 7+ years of dedicated data engineering experience, with at least 3 years actively managing end-to-end production pipelines on Hadoop clusters
- Proficiency working in Linux/Unix operating systems, shell scripting, and using code versioning tools like Git.
Experience, that will be an advantage:
- Core Big Data Frameworks: Hands-on mastery of HDFS, MapReduce, YARN, Hive.
- Distributed Compute: Advanced proficiency in Apache Spark using Python (PySpark) or Scala.
- Streaming & Ingestion: Deep operational knowledge of Apache Nifi or Kafka (brokers, producers, consumers, and schema registry).
- Programming Languages: Strong foundations in Python, SQL, and Scala or Java.
- NoSQL Database Systems: Direct experience handling unstructured/semi-structured data architectures in HBase, MongoDB, or Cassandra.
- Data Warehouse & Modelling: Strong grasp of dimensional data modelling, star/snowflake schemas, and data governance practices.
Limitation and Disclaimer:
The aforementioned job description is aimed to provide an overview of the overall type and level of work being performed for this role; it shouldn't be interpreted as a complete list of all obligations, responsibilities, and qualifications needed for the job.
In no way does this job description imply or say that these are the exclusive responsibilities of the employee holding this position. Any additional job-related instructions or tasks that are requested by the manager must be completed by the employee.
Ready, set, apply!
