Lead Data Engineer

TEKsystems Global Services in India; Hyderabad; On-site; INR 1,500,000 - 2,200,000; Full time

Job summary

TEKsystems Global Services in India is seeking an experienced data engineering professional to oversee end-to-end data pipelines, ensure data quality and governance, and drive scalable, auditable data solutions. The role emphasizes collaboration with stakeholders and enforcing coding standards.

You will mentor a team of data engineers, data analysts, and quality analysts while optimizing Spark-based transformations, SQL queries, and ETL/ELT processes for finance and reporting data.

Qualifications

• Hands-on delivery oversight of data pipelines, SQL queries, ETL/ELT logic, Spark jobs and reporting outputs.
• Mentor junior data engineers, data analysts and data quality analysts through daily guidance and reviews.
• Define data quality controls, reconciliation, anomaly checks, lineage awareness and issue triage for finance/reporting data.
• Enforce coding standards, documentation, reusable patterns and production-readiness of data solutions.
• Communicate risks, blockers, dependencies and corrective actions to delivery stakeholders.

Responsibilities

• Review pipelines, SQL, ETL/ELT logic, Spark jobs and reporting outputs.
• Provide day-to-day mentorship and troubleshooting guidance to team members.
• Establish and monitor data quality checks and data lineage for finance/reporting data.
• Govern data solutions with coding standards, documentation and reusable patterns.
• Communicate risks and blockers to delivery and programme stakeholders.

Skills

SQL

Python

ETL/ELT

Apache Spark

Scala / PySpark

Data modelling

Data quality

Data governance

Stakeholder communication

Mentoring

Engineering governance

Troubleshooting

Tools

Spark

Python tooling

Job description

• 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.