Lead Data Engineer

Lead Data Engineer

Notice period- Should be immediate

Experience -7 to 25 years

Location- Hyderabad

Work mode- All 5 days' work from office

CORE RESPONSIBILITIES

• Hands-on delivery oversight: review pipelines, SQL, ETL/ELT logic, Spark jobs, reporting outputs and technical deliverables.
• Mentor junior talent: coach data engineers, data analysts and data quality analysts through daily guidance, reviews and troubleshooting.
• Data quality and controls: define validation, reconciliation, anomaly checks, lineage awareness and issue triage for finance/reporting data.
• Engineering governance: enforce coding standards, documentation, reusable patterns, production-readiness and maintainable data solutions.
• Stakeholder communication: clearly communicate risks, blockers, dependencies and corrective actions to delivery and programme stakeholders.
• SQL: advanced joins, window functions, query tuning, data validation and troubleshooting.
• Python: scripting, data processing, API invocation, automation and pipeline support.
• ETL/ELT: ingestion, transformation, scheduling, monitoring, error handling and operational support.
• Apache Spark: Spark SQL, DataFrames/Datasets, batch pipelines, structured-streaming concepts, partitioning and performance tuning basics.
• Scala / PySpark: ability to understand or develop Spark transformations using Scala or PySpark; Scala hands-on preferred where Spark workloads are Scala-based.
• Data modelling and governance: warehousing basics, quality checks, reconciliation, auditability and traceable reporting outputs.

• 7 to 25 years of experience
• Immediate joiners (notice period immediate)
• Based in Hyderabad, working from office all 5 days
• Advanced SQL: joins, window functions, query tuning, data validation and troubleshooting
• Python: scripting, data processing, API invocation, automation and pipeline support
• ETL/ELT: ingestion, transformation, scheduling, monitoring, error handling and operational support
• Apache Spark: Spark SQL, DataFrames/Datasets, batch pipelines, structured-streaming concepts, partitioning and performance tuning basics
• Scala/PySpark: understand or develop Spark transformations; Scala hands-on preferred where workloads are Scala-based
• Data modelling and governance: warehousing basics, quality checks, reconciliation, auditability, traceable reporting outputs
• Hands-on delivery oversight of pipelines, SQL, ETL/ELT logic, Spark jobs and technical deliverables
• Mentoring and coaching data engineers, analysts and data quality analysts
• Stakeholder communication on risks, blockers, dependencies and corrective actions
• Engineering governance: coding standards, documentation, reusable patterns, production-readiness