Data Engineering Lead.
This mandate is run by SYMPHONI HR, a Mumbai-based executive search firm, est. 2003. Applications are email-verified and reach the search team running the role — confidentially, always.
About the organisation.
Our client is a leading global firm in the accounting and finance industry, renowned for its comprehensive professional services. They operate on a large scale, serving a diverse international clientele with innovative solutions. The company fosters a culture of continuous learning, collaboration, and excellence, empowering its employees to drive impact.
About the role.
The Data Engineering Lead will be a pivotal member of the analytics data platform team, focusing on ensuring the reliability and performance of data delivery. This role involves leading the resolution of customer-reported issues, maintaining robust data pipelines, and driving continuous improvements. You will be instrumental in troubleshooting complex data challenges and collaborating with various teams to enhance data accuracy and usability.
What you will do.
- Act as the primary technical lead for diagnosing, troubleshooting, and resolving complex customer-facing data and pipeline issues.
- Conduct root cause analysis (RCA) and implement long-term fixes to prevent recurrence of data issues.
- Collaborate closely with cross-functional teams to ensure data integrations meet customer expectations, with strong emphasis on data accuracy, consistency, and usability.
- Establish and lead structured incident management processes, including on-call practices, SLAs, and post-incident reviews to improve system reliability and customer satisfaction.
- Lead the design, build, and continuous improvement of scalable ETL/ELT pipelines with a strong focus on resolving customer issues.
- Ensure high availability, reliability, and data integrity in production environments for customer-critical data solutions.
What you bring.
- Strong expertise in Python and Java with a focus on building production-grade, customer-facing data solutions.
- Proven experience with Apache Spark and Kafka in managing large-scale, real-time and batch data pipelines in production.
- Deep understanding of production support, incident management, and root cause analysis (RCA) in distributed systems.
- Strong proficiency in SQL and performance tuning for customer-critical workloads.
- Hands-on experience with microservices architecture, with emphasis on reliability, observability, and fault tolerance.
- Expertise in big data technologies (Hive, HBase, Parquet) for scalable and reliable data storage.
- Strong experience in data quality frameworks, validation, and reconciliation processes.
- Familiarity with monitoring and observability tools (e.g., logs, metrics, tracing) to proactively identify and resolve issues.
- Excellent problem-solving and debugging skills in high-pressure, customer-impacting scenarios.
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