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ETL Architect

ETL Architect

HCLSoftware

8 years

0 Lacs

pune maharashtra india

Posted:4 days ago| Platform:

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Skills Required

etl ai data architecture standardization integration scalability governance model design processing metrics monitoring security analytics ml engineering orchestration adf informatica talend sql optimization airflow python tooling aws azure versioning rollback documentation kafka pipeline

Work Mode

On-site

Job Type

Full Time

Job Description

AI and Data ETL Architect

If you are available for a Face to face interview on10th Jan Saturday In Pune. Then please share your CV to monica_sharma@hcl-software.com with the below details:

Total Experience-

Current CTC-

Expected CTC-

Notice period-

If shortlisted you will be invited for a F2F interview

Role Context

Own ETL architecture patterns, reusable frameworks, and standardization for multi-source data integration. Ensures scalability, maintainability, and governance across ETL delivery, aligning to product roadmap and data model strategy.

Key Responsibilities

Define reference architecture for ETL/ELT pipelines: ingestion → transform → serving layers.
Establish standards for metadata/config-driven ETL, naming, error handling, and replay.
Design scalable patterns for CDC, incremental processing, backfills, and dependency orchestration.
Define CI/CD patterns for ETL and govern environment promotions.
Define observability standards: logs, metrics, lineage, data quality monitoring, SLAs.
Review designs for performance, cost, security, and operational readiness.
Align ETL patterns with data modelling and downstream analytics/ML needs.

Must Have

8 to 12 years in data engineering with strong ETL architecture ownership.
Expertise across ETL tools/frameworks (dbt + orchestration, ADF/Glue/Informatica/Talend etc.).
Strong SQL performance and warehouse optimization knowledge.
Airflow (or equivalent) architecture-level orchestration design.
Python capability for framework/tooling design and automation.
Strong knowledge of cloud data architecture (AWS/Azure/GCP).
Delivery engineering maturity: CI/CD, versioning, release discipline, rollback strategies.
Non-technical: strong documentation, workshop facilitation, ability to influence teams.

Good To Have

Experience with event streaming architectures (Kafka) and lakehouse patterns.
Exposure to data catalog/lineage tooling and governance practices.
Familiarity with ML pipeline needs (feature readiness, time semantics).

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