ETL SQL​/Enterprise Data Framework Developer

ETL SQL / Enterprise Data Framework Developer

Location:

Hyderabad

Experience:

3–6 Years

Employment Type:

Full-Time

Job Summary
We are seeking an experienced  ETL SQL / Data Engineer  with strong expertise in  SQL development, ETL data pipelines, Stored Procedures, Views, Autosys scheduling, and Enterprise Data Framework (EDF) .
The candidate will be responsible for developing and maintaining data pipelines that extract data from multiple source systems, process and validate the data through EDF modules, and load it into target platforms such as  Data Lake and Snowflake .
The ideal candidate should have strong SQL coding skills, hands-on experience with  DBeaver , and a good understanding of data integration using  flat files and REST APIs . Experience working with enterprise-scale data pipelines and scheduling frameworks is highly desirable.

Key Responsibilities
ETL & SQL Development
Develop, maintain, and optimize  ETL SQL code  for enterprise data pipelines.
Write complex SQL queries for data extraction, transformation, validation, reconciliation, and loading.
Develop and maintain:
Stored Procedures
Views
SQL scripts
Data transformation logic
Data validation and reconciliation queries
Analyze source data and determine appropriate transformation and mapping logic.
Optimize SQL queries for performance and scalability.
Perform data quality checks and troubleshoot data discrepancies.
Ensure data pipelines meet defined business and technical requirements.
Enterprise Data Framework (EDF)
Develop new  Enterprise Data Framework (EDF) jobs  to support enterprise data processing requirements.
Work extensively with EDF to design, build, test, deploy, and support data pipelines.
Understand and utilize EDF modules including:
Data Processing Pipeline (D2P)
Data Control and Anomaly Detection Framework (DCAF)
General-Purpose Data Reconciliation (GPR)
Build EDF jobs to extract data from source systems and process the data through the appropriate EDF modules.
Configure data processing, validation, anomaly detection, and reconciliation rules.
Ensure successful movement of data across different stages of the pipeline.
Troubleshoot EDF job failures and data processing issues.
Develop reusable and maintainable EDF components wherever possible.
Data Extraction & Integration
Build data extraction processes from multiple source systems.
Work with  flat files  as source data, including file-based ingestion and processing.
Develop integrations using  REST APIs  for data ingestion.
Understand API request/response structures and troubleshoot API-related data ingestion issues.
Validate incoming data for completeness, accuracy, format, and quality.
Handle different data formats and transformation requirements.
Ensure reliable movement of data from source systems through the EDF pipeline.
Data Processing & Transformation
Process extracted data through EDF's D2P, DCAF, and GPR modules.
Implement transformation and business rules required for downstream processing.
Develop data validation and anomaly detection mechanisms.
Implement reconciliation logic to compare source and target datasets.
Investigate data quality issues and work with upstream teams to resolve them.
Ensure data is accurately transformed before loading into target environments.
Target Data Platforms
Build and support pipelines that load processed data into target environments such as:
Data Lake
Snowflake
Validate successful data loading and perform post-load data quality checks.
Reconcile source and target records to ensure completeness and accuracy.
Troubleshoot data load failures and performance issues.
Work with Data Engineering and platform teams to resolve target-system issues.
Autosys Scheduling
Develop, configure, and maintain  Autosys jobs  for data pipeline scheduling.
Define job dependencies, calendars, conditions, and execution sequences.
Monitor scheduled ETL and EDF jobs.
Troubleshoot failed or delayed jobs.
Manage dependencies between upstream and downstream data processes.
Support production scheduling and batch-processing requirements.
Data Quality & Reconciliation
Implement data quality checks throughout the ETL lifecycle.
Utilize  DCAF  capabilities for anomaly detection and data control.
Develop…