This PowerPoint presentation provides a comprehensive analysis of salary trends across various data-centric job roles, utilizing PostgreSQL and Tableau. It highlights the top and bottom salary brackets in the industry, showing the correlation between job specialization, demand, and compensation. Additionally, it presents the frequency of job postings for these roles, offering insights into the data job market's landscape. This presentation is designed to showcase my analytical skills and proficiency with data tools for a resume project.

1. ANALYTICAL
PROJECT: DATA JOB
SALARIES
Natalie Offenbacker
12-6-23

2. UNVEILING TRENDS IN DATA JOB SALARIES: A SQL AND
TABLEAU JOURNEY
Welcome to my analytical exploration of data job salaries. This presentation
will focus on the fascinating world of data science and analytics, where insights are
hidden within salary data for years 2020-2023. This analysis aims to not only
demonstrate data but journeying through the nuances of the data job market. The
ultimate goal is to uncover and understand the various factors influencing global
data jobs. With careful analyzation, I aim to reveal trends over time, and the impact
of different experience levels on compensation.
As a part of my portfolio project, this presentation is designed to showcase
my analytical skills and my proficiency in using key data analysis tools. With that
being said Let's Begin!
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3. TOOLS USED
In this journey, I leveraged the power of SQL
through PGAdmin for in depth data extraction
and analysis. This was complemented using
Tableau for the creation of compelling
visualizations, transforming the raw data into
understandable and engaging graphical
representations.
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4. 20XX 4
THE DATA

5. A DATASET OVERVIEW
Data Job Salaries dataset
has a total of 8061 records
and 11 columns starting with
“Work Year” and ending
“Company Size”. This dataset
provides a unique opportunity
to extract meaningful insights
and trends with the rapidly
evolving data job market.
EXPERIE N CE LEVEL | EM PLOY M ENT
T Y PE
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6. TRENDS OVER TIME
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The dataset, encompasses records from 2020 to 2023. To lay the groundwork for an in-
depth salary analysis, I started with querying the yearly counts, revealing a fascinating trend in the
volume of data over time. The year 2020 had a modest count of 75 records, followed by a
moderate increase to 218 records in 2021. The year 2022 marked a significant leap, introducing
1650 records. However, it is 2023 that dominates the dataset, contributing a substantial 6118
records. This escalation in data volume is pivotal for analysis as it not only reflects the growing
comprehensiveness of the dataset but also underscores the need to consider the increasing
representation of recent years in any trend analysis. Such a distribution of records across years
necessitates a weighted approach in interpreting salary trends, ensuring that insights drawn are
reflective of both the expanding dataset and the evolving landscape of data job salaries.

7. TRENDS OVER TIME CONTINUED
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Using the PGAdmin’s Query Tool, a clear picture
of the dataset’s composition over time was
created. It was crafted to select and count all
records from the “work_year” column within the
“data_j” table. To further refine the output, records
were grouped by “work_year” This is a crucial
step in a query because it allows for organized
aggregation. The final step was to neatly arrange
grouped data in ascending order using the
ORDER BY clause. The ordering enhances
readability and gives records a chronological
outlook.

8. TRENDS OVER TIME: OVERALL TREND
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Building upon the understanding established by
the initial query, a deeper dive into the dataset
revealed insightful salary trends throughout the
years. In 2020, amidst 75 records, the average
salary stood at $102,251. Interestingly in the year
2021 with its 218 records, the average slightly
deviated from an assumed upward trajectory with
a salary average of $99,922. As the dataset
expanded in volume, with 1650 records in 2022
and 6118 records in 2023, the average salary
witnessed a significant ascent as well. 2022 had
recorded an average salary of $134,508 and 2023
escalated to $155,713, certainly an exponential
growth.

9. 20XX 9

10. TRENDS OVER TIME: OVERALL TREND
The pattern of salary progression particularly on the dip in 2021 followed
by a sharp increase offers an awesome ground for analysis. This pattern
suggests external factors that might have influenced fluctuations, such as
economic trends, industry shifts, and even changes in demand for data
jobs. This growth trend provides a multi-dimensional view of the data
industry as well as giving a broad and understandable perspective.
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11. TRENDS OVER TIME: EXPERIENCE LEVEL
Delving deeper into a classification of experience
level offers a data analyst the opportunity to
decipher which experience bracket predominantly
shapes the salary landscape over time. This
examination will reveal the narrative of
professional growth and its correspondence in the
data job market. A broad overview of overall
salaries for experience levels is shown and then a
better in-depth look is conducted.
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**note: A simple average taken from a set of
grouped data, such as average salaries by
experience level, may not represent the larger
data set’s central tendency if it does not account
for the numbers of records within each group. In
this analysis the simple average across different
experience levels for 2020 was around
$120,079. However, when taking account all
individual salary entries for the year in
PGAdmin, the average was calculated at

12. TRENDS OVER TIME: EXPERIENCE LEVEL
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Using the PGAdmin’s Query Tool, a clear picture
of the dataset’s composition over time was
created. It was crafted to select “work_year” and
count all records from the “experience_level”
column within the “data_j” table. In order to get an
appropriate average salary, a column was crafted
to be rounded by 2 decimal places. To further
refine the output, records were grouped by
“work_year and “experience_level”. The final step
was to neatly arrange grouped data in ascending
order using the ORDER BY clause. The ordering
enhances readability and gives records a
chronological outlook.

13. TRENDS OVER TIME: EXPERIENCE LEVEL
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The data extracted from the SQL query narrates a compelling progression of salary trends across the
different experience levels. A general positive trend in salaries across the board is evident. However, in
2021 there was an unexpected deviation from the trend: Mid-level positions saw a salary reduction by
over $5000 and Entry-level positions experienced a $6000 decrease. Senior level positions took the
largest hit in salary reduction with a $12,700 decrease. This dip in salaries for these levels during 2021
could be indictive of market fluctuations or even shifts in demand for data job roles. After 2021 the
upward salary trend for both levels resumes, signaling a recovery or an adjustment to the previous
year’s anomaly. The data also narrates mid-level roles as the most popular in terms of job counts.
Starting in 2022 a paradigm shift occurred when senior-level positions took the forefront in counts. This
may possibly reflect a maturing market that values and requires more experienced professionals. The
next two slides show compelling graphs visualizing an overall average salary for each
experience level and then a breakdown of what made up the overall salaries.

14. 20XX Pitch Deck 14

15. 20XX Pitch Deck 15

16. TRENDS OVER TIME: JOB TITLE
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Knowing the minimum and maximum salaries for
job titles is important for competitive
compensation strategies. It helps employers offer
attractive salaries and assists job seekers in
making informed decisions. These figures can
also reflect on the required expertise and
responsibilities of different roles. The salary
ranges help identify data anomalies and market
outliers, which contributes to accurate market
analysis. When the job title data is combined with
job counts, it provides insights into employment
trends and possibly economic conditions.
Within the dataset of 8061 entries, a unique count
of 118 distinct job titles has been recorded. The
dataset’s comprehensive scope provides an
informational lens for examining industry salary
norms and identifying employment trends.
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17. TRENDS OVER TIME: JOB TITLE
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Using the PGAdmin’s Query Tool, a query was
crafted to get a clear idea of the variety of jobs in
the dataset as well as providing an exact count.
The precise extraction of the data helps illuminate
the snapshot of the professional landscape
captured by the dataset.

18. TRENDS OVER TIME: JOB TITLE
Pitch Deck 18
As previously highlighted, the significance of
understanding the salary range for job titles is
paramount for shaping competitive compensation
strategies, pinpointing outliers, and analyzing
employment patterns. To facilitate a deeper
comprehension of each of these aspects, a SQL
aggregate query was crafted to extract all records
pertaining to the aspects mentioned.

19. 20XX Pitch Deck 19

20. TRENDS OVER TIME: JOB TITLE
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This visual was created to share an overall analysis of data jobs and is segmented into the top highest
positions and the bottom 10. At the top we see Analytics Engineering Manager with the overall highest
average salary. The upper salaries also showcase a variety of special roles which possibly indicates a
niche expertise in leadership. In contrast, the bottom 10 jobs, while still essential in the data industry
yield significant lower average salaries. Roles such as Data Quality Engineer and Principal Data
Architect are at the lower end of the salary spectrum. This could reflect factors such as market demand,
complexity in role, and even location of where job is at. The tree map included within the dashboard
highlights job distribution within the dataset. Based on the visual, data engineers and data analysts lead
the pack suggesting they are high in demand. Overall, the dashboard of salary and job availability
underscores the multifaceted nature of the data job market, where salary potential often correlates with
specialized roles and the level of responsibility.

21. TRENDS OVER TIME: JOB TITLE
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This analysis, utilizing SQL for data extraction and Tableau for visualization, aimed to reveal insightful
trends in data job salaries from 2020 to 2023. Key findings included a notable dip in salaries in 2021,
followed its recovery, and a significant variation between experience levels and job titles. This project
was created in hopes of showcasing my proficiency in handling and interpreting datasets. My ability to
derive meaningful insights using PostgreSQL and Tableau is shown throughout this analysis
and aims to demonstrate my readiness to also contribute effectively to a data driven role!

22. THANK YOU
Natalie Offenbacker
natkcn07@gmail.com
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