Data Engineer (CE60SF RM 3570)

Position: Data Engineer (CE60SF RM 3570)

Shift timing : General Shift
Relevant Experience required : 4+ years
Education Required : Bachelor’s / Masters / PhD : B.E Computers, MCA is preferable

Must have skills:

Azure Data Factory and Databricks (PySpark/SQL)

Python (pandas/PySpark)

SQL

CI/CD (Azure DevOps/GitHub Actions/Jenkins).

exposure to drift detection and pipeline observability

Cloud platform [Azure preferred: ADLS Gen2, Key Vault, Databricks, ADF basics]

Data Governance & Security

Good to have:

Databricks Asset Bundles (DAB)

Power BI exposure

Streaming/real-time: Kafka/Event Hubs; CDC tools (e.g., Debezium, ADF/Synapse CDC).

MLOps touchpoints: MLflow tracking/registry, feature tables, basic model-inference pipelines.

Role Summary
Build and operate scalable, reliable data pipelines on Azure. Develop batch and streaming ingestion, transform data using Databricks (PySpark/SQL), enforce data quality, and publish curated datasets for analytics and ML.

Key Responsibilities

Design, build, and maintain ETL/ELT pipelines in Azure Data Factory and Databricks across Bronze Silver Gold layers.

Implement Delta Lake best practices (ACID, schema evolution, MERGE/upsert, time travel, Z-ORDER).

Write performant PySpark and SQL; tune jobs (partitioning, caching, join strategies).

Create reusable components; manage code in Git; contribute to CI/CD pipelines (Azure DevOps/GitHub Actions/Jenkins).

Apply data quality checks (Great Expectations or custom validations), monitoring, drift detection, and alerting.

Model data for analytics (star/dimensional); publish to Synapse/Snowflake/SQL Server.

Uphold governance and security (Purview/Unity Catalog lineage, RBAC, tagging, encryption, PII handling).

Author documentation/runbooks; support production incidents and root-cause analysis; suggest
cost/performance improvements.

Must-Have (Mandatory)

Data Engineering & Pipelines
o Hands-on experience building production pipelines with Azure Data Factory and Databricks (PySpark/SQL). o Working knowledge of Medallion Architecture and Delta Lake (schema evolution, ACID).

Programming & Automation
o Strong Python (pandas/PySpark) and SQL. o Practical Git workflow; experience integrating pipelines into CI/CD (Azure DevOps/GitHub Actions/Jenkins). o Familiarity with packaging reusable code (e.g., Python wheels) and configuration-driven jobs.

Data Modelling & Warehousing
o Solid grasp of dimensional modelling/star schemas; experience with Synapse, Snowflake, or SQL Server.

Data Quality & Monitoring
o Implemented validation checks and alerts; exposure to drift detection and pipeline observability.

Cloud Platforms (Azure preferred)
o ADLS Gen2, Key Vault, Databricks, ADF basics (linked services, datasets, triggers), environment promotion.

Data Governance & Security
o Experience with metadata/lineage (Purview/Unity Catalog), RBAC, secrets management, and secure data sharing. o Understanding of PII/PHI handling and encryption at rest/in transit.

Collaboration
o Clear communication, documentation discipline, Agile ways of working, and code reviews.

Good-to-Have
o Databricks Asset Bundles (DAB) for environment promotion/infra-as-code style deployments. o Streaming/real-time: Kafka/Event Hubs; CDC tools (e.g., Debezium, ADF/Synapse CDC). o MLOps touchpoints: MLflow tracking/registry, feature tables, basic model-inference pipelines. o Power BI exposure for publishing curated tables and building operational KPIs. o DataOps practices: automated testing, data contracts, lineage-aware deployments, cost optimization on Azure. o Certifications: Microsoft Certified — Azure Data Engineer Associate (DP-203) or equivalent.

Qualifications

4–6 years of professional experience in data engineering (or equivalent project depth).

Bachelor’s/Master’s in CS/IT/Engineering or related field (or equivalent practical experience).

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