Job Description
Job title:Hadoop, Spark Engineer — Apache Spark / Scala / Hadoop
Role Summary:
We are seeking an experienced Big Data Engineer to design, build, and optimize large-scale batch and streaming data pipelines on the Hadoop ecosystem using Apache Spark and Scala. The role supports high-volume ingestion, transformation, and enrichment of clickstream, network, and location datasets, working closely with data architects, platform engineering, and downstream analytics teams. This is a hands-on engineering role with ownership of pipeline performance, reliability, and data quality in production.
Key Responsibilities
- Design, develop, and maintain distributed data pipelines using Apache Spark (Core, SQL, Streaming) written in Scala.
- Build ingestion and transformation workflows across the Hadoop ecosystem — HDFS, Hive, YARN, MapReduce — for structured and semi-structured data at TB–PB scale.
- Tune and optimize Spark jobs: partitioning strategy, caching, broadcast joins, shuffle reduction, data skew handling, and executor/memory sizing.
- Implement real-time and near-real-time ingestion using Apache NiFi and/or Kafka.
- Embed data quality, reconciliation, and validation controls directly into pipelines.
- Author and optimize HiveQL and Spark SQL for curated and consumption layers.
- Automate orchestration and scheduling using Airflow, Oozie, or Control-M.
- Participate in code reviews, CI/CD automation, unit and integration testing, and production support.
- Troubleshoot job failures, SLA breaches, and performance regressions; drive root-cause analysis to permanent fixes.
- Document data flows, lineage, transformation logic, and operational runbooks.
Required Qualifications:
- 4+ years of data engineering experience, with 2+ years hands-on Apache Spark development in Scala on production workloads.
- Strong Scala fundamentals — functional programming constructs, collections API, case classes, pattern matching, implicit, and error handling.
- Deep working knowledge of the Hadoop ecosystem: HDFS, Hive, YARN, HBase.
- Advanced SQL and data modeling skills across dimensional and big-data denormalized patterns.
- Demonstrated Spark performance tuning and debugging using the Spark UI, event logs, and physical execution plans.
- Proficiency with columnar and serialization formats — Parquet, ORC, Avro — including compression and partitioning trade-offs.
- Linux and shell scripting, Git, Maven or SBT, and Jenkins or equivalent CI/CD tooling.
- Ability to work independently in a distributed onshore–offshore delivery model.
Preferred Qualifications:
- Kafka and Spark Structured Streaming for event-driven pipelines.
- Cloud data platform exposure — GCP (BigQuery, Dataproc), AWS EMR, or Azure Databricks.
- Telecom domain experience with clickstream, network, or geospatial/location data.
- Python or PySpark as a secondary development language.
- Data governance and security frameworks — Apache Ranger, Kerberos, PII masking and tokenization
Nice to Have:
- Apache NiFi flow design, configuration, and administration.
Education:
Bachelor's degree in computer science, Information Technology, Engineering, or a related discipline — or equivalent demonstrable practical experience.
Qualifications
Bachelor's
Range Of Year Experience-Min Year
4
Range Of Year Experience-Max Year
6
