BE | BTech | MCA | OR equivalent degree in Computer Science, IT or a related field candidates having 5 to 10 years of experience in Python, PySpark, SQL Unix + Big Data (Hadoop + Hive), Airflow, Datawarehousing, Data modelling.

Required skills :

- Strong hands-on experience in Python Development.

- Extensive experience with PySpark / Apache Spark.

- Expertise in Data Engineering, SQL Unix + Big Data (Hadoop + Hive), Airflow, Datawarehousing, Data modelling and data processing frameworks.

- Strong knowledge of SQL and database concepts.

- Experience working with large-scale datasets in distributed environments.

- Understanding of Data Warehousing concepts and dimensional data modeling.

- Exposure to cloud platforms such as Azure, AWS, or GCP.

- Experience with version control tools such as Git.

- Experience with Kubernetes, Java or python + Spark SQL, Delta Lake.

- Knowledge of Airflow, ADF, or other orchestration tools.

- Experience in CI/CD implementation.

- Familiarity with SAP BW/4HANA, SAP S/4HANA, or SAP Banking solutions.

SAP Skills :

- Exposure to SAP FSDM/FPSL.

- Exposure to SAP Financial Services Data Management (FSDM).

- Understanding of SAP FSPL (Financial Services Package for Liquidity) + Big Data (Hadoop + Hive) processes and data structures.

- Knowledge of financial services, banking, risk, liquidity, or regulatory reporting domains is preferred.

Role & Responsibilities :

- Partner closely with Product Managers, Data Scientists, and AdOps teams in an Agile/Scrum environment to translate ambiguous business requirements into scalable, production-grade ETL/ELT data pipelines and dimensional data models.

- Design and maintain end-to-end cloud Lakehouse architectures on Python using PySpark, Databricks, and Delta Lake, establishing standardized frameworks for automated data ingestion, schema enforcement, and data reconciliation.

- Manage production workflow orchestration using Apache Airflow, building automated failure recovery, log monitoring, and SLA-driven alerting to guarantee 99.9% data pipeline uptime and reliable downstream analytics delivery.

- Mentored junior data engineers through pair programming and code reviews; established team-wide engineering best practices around PySpark performance tuning, Git version control, and CI/CD pipelines.

- Conducted routine cluster cost optimizations and query tuning across cloud infrastructure, adjusting Spark driver/executor configurations, dynamic allocation, and storage layouts to maximize compute efficiency.