Lead Data Engineer

TEKsystems Global Services in India; Hyderabad; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

TEKsystems Global Services in India seeks a Lead Data Engineer to guide data pipelines, oversee SQL, ETL/ELT and Spark-based workloads from Hyderabad in a full-time, on-site capacity.

The role emphasizes mentoring junior engineers, ensuring data quality, establishing governance, and communicating risks to stakeholders. Immediate notice period and substantial experience are requested.

Qualifications

• 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.

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.

Job description

Lead Data Engineer

Notice period- Should be immediate only

Experience -7 to 25 years

Location- Hyderabad only

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.