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Tier-1 brand plus a common early-career Data Engineer profile drives medium competition.
Core data engineering skills are transferable, but fintech/finance domain knowledge raises fit sensitivity to medium.
1+ years requirement plus mandatory SQL, ETL, and data modeling increases shortlisting strictness to medium.
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Design, implement, and support a scalable platform providing secure access to large financial datasets across multiple regions (North America, Asia, Europe).
Collaborate with finance and accounting stakeholders to gather requirements and deliver business intelligence solutions including metrics, reports, dashboards, and analyses that influence day-to-day decisions.
Own data modeling, ETL pipeline development, performance tuning, and continuous improvement of reporting and analysis processes using AWS big data technologies.
Minimum 1+ years of data engineering experience.
Proficient in SQL and experience with data modeling, warehousing, and building ETL pipelines.
Experience with one or more query languages such as SQL, PL/SQL, HiveQL, SparkSQL, or Scala.
Experience with one or more scripting languages like Python or KornShell.
Experienced in designing and maintaining complex, large-scale data platforms with a focus on operational excellence and automated self-service support.
Comfortable working with cross-regional teams and diverse finance stakeholders to translate business needs into technical solutions.
Familiarity with big data ecosystems including Hadoop, Hive, Spark, EMR, and ETL tools, capable of innovating and scaling data architecture using AWS cloud technologies.
