The Role
Design, develop, optimize, and maintain scalable AWS data processing and ETL/ELT pipelines using Python, PySpark, Apache Spark, and SQL. Responsibilities include data extraction, transformation, validation, performance tuning, integration across databases, APIs, and cloud storage, monitoring, error handling, and data quality management. The role collaborates with engineering, architecture, analytics, and business teams while following Git, Agile, and software development best practices.
Summary Generated by Built In
Job Title: Data Engineer
Experience Level: 4 - 6 years
Location: Hyderabad / Pune
Experience Level: 4 - 6 years
Location: Hyderabad / Pune
Qualification: BTech/MTech/MCA
Mode of Work: Hybrid
We are seeking an experienced AWS Data
Engineer with deep expertise in Banking, Financial Services, and Insurance systems. The candidate
must be familiar at least in one Data ware housing concepts
such as ODS/Marts and must have done at least one end-to-end
data processing pipeline designing.
He/she should be capable of handling the ETL design and
implementation solely and would work towards creating a
positive and innovation friendly environment. The incumbent
would be responsible for creating new capabilities.
Responsibilities
- Develop, maintain, and optimize data processing applications using Python and PySpark.
- Build scalable ETL/ELT pipelines to process large volumes of structured and unstructured data.
- Perform data extraction, cleansing, transformation, aggregation, and validation.
- Work with Apache Spark for distributed data processing and large-scale data transformations.
- Write complex SQL queries and optimize database operations.
- Develop reusable Python modules, functions, and libraries.
- Optimize Spark jobs by analyzing execution plans, partitioning, caching, and performance bottlenecks.
- Work with file formats such as CSV, JSON, Parquet.
- Integrate data from multiple sources, including databases, APIs, and cloud storage.
- Implement error handling, logging, monitoring, and data quality checks.
- Collaborate with data engineers, data architects, analysts, and business stakeholders.
- Follow coding standards, version control practices, and Agile development methodologies.
Requirements
Requirements
- Strong programming knowledge in Python, including functions, OOP, exception handling, and data structures.
- Hands-on experience with PySpark DataFrames, Spark SQL, transformations, and actions.
- Good understanding of Apache Spark architecture and distributed computing.
- Strong SQL skills, including joins, sub queries, CTEs, and window functions.
- Experience developing ETL/ELT pipelines and handling large datasets.
- Knowledge of data processing formats such as Parquet, JSON, and CSV.
- Experience with Git and software development best practices.
- Understanding of debugging, performance tuning, and troubleshooting.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Familiarity with Databricks, Azure Data Factory, AWS Glue, or similar data engineering tools.
- Knowledge of data warehouses and lake house architectures.
- Experience with Airflow or other workflow orchestration tools.
- Familiarity with CI/CD pipelines, Docker, and automated testing.
- Knowledge of Delta Lake, Kafka, or streaming data processing.
Benefits
- Comprehensive Medical Coverage:Health insurance of INR 5.0 Lakhs for you and your family (up to 6 members), ensuring complete peace of mind.
- Robust Protection Plans:Group Personal Accident Insurance and Group Term Life Insurance to safeguard you and your loved ones.
- Retirement Benefits:PF and Gratuity provided as per standard government regulations.
- Flexible Work Options:Enjoy hybrid work arrangements & flexible working hours
- Generous Leave Policy:21 days of annual leave, in addition to 10 company-declared holidays.
- Employee Well-being Spaces:Access to a dedicated break-out area with round-the-clock refreshments for relaxation and rejuvenation.
Skills Required
- 4–6 years of professional experience
- BTech, MTech, or MCA degree
- Strong Python programming skills, including functions, object-oriented programming, exception handling, and data structures
- Hands-on experience with PySpark DataFrames, Spark SQL, transformations, and actions
- Understanding of Apache Spark architecture and distributed computing
- Strong SQL skills, including joins, subqueries, CTEs, and window functions
- Experience developing ETL/ELT pipelines and handling large datasets
- Knowledge of Parquet, JSON, and CSV data formats
- Experience with Git and software development best practices
- Understanding of debugging, performance tuning, and troubleshooting
- Experience with cloud platforms such as AWS, Azure, or Google Cloud
- Familiarity with Databricks, Azure Data Factory, AWS Glue, or similar data engineering tools
- Knowledge of data warehouses and lakehouse architectures
- Experience with Airflow or other workflow orchestration tools
- Familiarity with CI/CD pipelines, Docker, and automated testing
- Knowledge of Delta Lake, Kafka, or streaming data processing
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The Company
What We Do
DATAECONOMY is a global, cloud-first data and AI consultancy delivering enterprise-grade solutions through an innovative intellectual-property suite. Its work spans data and BI platform modernization, self-service AI, data mesh and fabric, master data management, governance, cloud enablement, digital engineering, knowledge graphs, and machine lakes supporting cybersecurity and financial-crime use cases for enterprise clients.
