Responsibilities
- Manage installation, configuration, upgrades, and patching of Microsoft SQL Server.
- Design and implement database structures, tables, views, indexes, and stored procedures.
- Perform performance tuning for queries, indexing, and overall database configuration.
- Manage backup and recovery strategies, and conduct regular disaster recovery tests.
- Ensure database security through roles, permissions, and data encryption.
- Monitor database health and performance using SQL Server native tools (Profiler, Activity Monitor, DMVs) or third-party monitoring tools.
- Handle incidents and troubleshoot database issues (deadlocks, blocking, slow query performance).
- Support developers by providing query optimization and data modeling assistance.
- Maintain high availability using technologies such as Always On, Database Mirroring, Failover Clustering, and Replication.
- Create and maintain technical documentation related to configurations, procedures, and database standardization.
Qualifications
- Bachelor's or Master's degree in Computer Science, Information Systems, Data Engineering, or related field.
- Minimum
5+ years of experience
in data engineering or data platform development, preferably in the
banking, fintech, or financial services
sector. - Proficiency in
SQL
and one or more programming languages such as
Python, Java, or Scala
. - Strong experience with
ETL/ELT tools
(e.g., Apache Airflow, Talend, Informatica, or NiFi). - Deep understanding of
data warehouse and lake architectures
(e.g., Snowflake, BigQuery, Redshift, Hive, Delta Lake). - Experience with
streaming and real-time data processing frameworks
(e.g., Apache Kafka, Spark Streaming, Flink). - Strong knowledge of
database technologies
(PostgreSQL, Oracle, MySQL, MongoDB) and
data modeling techniques
(dimensional modeling, star schema). - Experience with
cloud-based data solutions
(AWS, GCP, Azure) — especially storage, compute, and orchestration services. - Familiarity with
containerization and DevOps tools
(Docker, Kubernetes, CI/CD pipelines). - Advanced certifications in Data Engineering, Cloud, or Big Data technologies are a plus (e.g., AWS Certified Data Engineer, GCP Professional Data Engineer).