Data Analyst

As a Data Analyst at Siepe, you will be responsible for developing and maintaining position and trade reconciliation reports, troubleshooting data discrepancies, and working collaboratively with various teams to enhance operational efficiency. Your analytical skills will come into play as you investigate and resolve complex reconciliation issues while ensuring data accuracy.

To be considered for the Data Analyst position at Siepe, a Bachelor’s degree in Accounting, Finance, MIS, Computer Science, Engineering, or a related analytical field is required. Though 0-2 years of experience in data analysis or financial operations support is preferred, strong analytical skills and a proactive attitude toward problem-solving are key!

Key skills for a Data Analyst at Siepe include strong analytical and problem-solving abilities, high attention to detail, and proficiency in SQL. Effective communication skills for collaboration across teams, as well as adaptability in a fast-paced environment, are also crucial for success in this role.

The work environment for a Data Analyst at Siepe is collaborative and vibrant. You’ll be interacting with various teams and will work in a fast-paced, operations-driven atmosphere where your contributions directly impact the business. Enjoy a supportive culture that values teamwork, innovation, and personal development!

Siepe offers a comprehensive benefits package, including paid holidays and vacation, 401k matching, and bonus opportunities. Employees also enjoy daily catered lunches, professional development programs, and an engaging work culture with regular celebrations and team bonding activities.

In approaching data reconciliation tasks, I first ensure I thoroughly understand the datasets involved. I prioritize accuracy by reviewing discrepancies with a methodical mindset, using SQL to trace the issues to their source. Collaboration with stakeholders is key, as it allows me to develop actionable insights and implement preventive measures against recurring discrepancies.

In a previous project, I encountered a pattern of discrepancies affecting trade data integrity. By diving deep into SQL queries, I discovered issues stemming from automated feed errors. I built a revised process that not only resolved immediate issues but also introduced checks that significantly reduced future discrepancies.

I am proficient in SQL for data manipulation and analysis, as well as data visualization tools like Tableau or Power BI. Additionally, I have experience with Excel for complex data analysis and calculations, which I find invaluable for quick reporting and presentations.

To ensure data accuracy, I always validate input datasets before analysis, implement checks during the reporting process, and compare outputs against expected results. I also maintain clear documentation to track data sources and methodologies, facilitating troubleshooting when discrepancies arise.

Communication is incredibly important as a Data Analyst. It allows me to effectively collaborate with cross-functional teams, understand their data needs, and convey complex analytical findings in an accessible manner. Good communication helps bridge gaps, ensuring that all stakeholders are aligned on objectives and outcomes.

To automate reconciliation processes, I analyze existing workflows to identify repetitive tasks that can be streamlined. Then, I determine the right technology tools, such as SQL scripts, to automate these tasks, ensuring I set up monitoring systems that notify me of any discrepancies in real-time.

I have experience supporting financial operations through data analysis by maintaining accurate financial datasets and providing regular reports that inform decision-making. My background in finance has equipped me with an understanding of key metrics and reconciliation processes essential for effective financial management.

Adaptability is crucial in fast-paced environments. I prioritize tasks effectively, maintain a cool head under pressure, and stay organized. Regularly reviewing and adjusting my approach helps me remain agile, ensuring that I can rapidly respond to changing priorities while delivering timely analyses.

When handling discrepancies, I take a systematic approach by first investigating the source of the error. I collaborate with relevant teams to gather context while documenting my findings. Once identified, I work towards resolving the issue and implementing solutions to prevent recurrence.

My passion for data analysis stems from my desire to turn raw data into actionable insights that propel business decisions. I enjoy the challenge of problem-solving and the satisfaction that comes from providing clarity and direction through data, contributing to a company’s success.