5X named Leader in DataOps!
5X named Leader in DataOps!
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Before diving into the insights, let’s glance at the methodology we adopted to gather all the data:
We used our comprehensive database of salary information from various sources.
We extracted data from reputable career websites, job boards, and industry reports.
We dug insights from a targeted survey conducted among data professionals.
Note:
The figures are in US Dollars (USD) for consistency.
The salaries in this article are the total cost to the company (CTC) for hiring a specific data role.
Some of the wide pay ranges are attributed to data salaries depending on factors like location, industry, years of experience, company size, and specific skills.
The data is sourced from verified profiles on salary boards like 6figr, Glassdoor, Payscale, and Builtin and may not represent all data professionals.
There is a clear correlation between company size & average total compensation.
Geographical location has the most significant impact on earning potential.
North America reported the highest average total compensation, with San Francisco emerging as a hotspot for lucrative data roles.
Denver has the lowest adjusted salaries across all roles after applying PPP (assuming the PPP index relative to New York).
Male data professionals earn, on average, 10-20% more than their female counterparts.
Data professionals with expertise in specific tools earn more than those with broader skill set.
The average salaries for data professionals have witnessed steady growth across technology, finance, healthcare, and e-commerce industries.
Salaries can differ in B2B, D2C, and other sectors based on factors like job complexity, competition, and the data culture.
Data salaries change depending on how much a company has grown. Startups that are just starting out might offer shares in the company as part of the pay, while bigger, more established companies usually give competitive salaries.
Average salaries increase with larger company sizes, reflecting the added responsibilities and complexity associated with managing large datasets and teams.
When we look at cities like New York, Austin, Seattle, and Denver, the average salaries vary greatly. Usually, cities with big tech industries near the coast pay more.
Companies that make more money often pay better, especially for senior data positions.
Despite progress in workplace equality, women in data roles still earn less than men.
Salary determinants: Specialized skills (market analysis, customer segmentation, or financial modelling), experience & seniority, geographic location, tool proficiency
Salary determinants: Experience level, geographic location, specialized sKills, industry, and project complexity
Salary determinants: dbt expertise, industry demand, experience level, geographic location
Salary determinants: Skillset (advanced statistical analysis, ML, data manipulation and visualization), specialization & niche skills (NLP, computer vision, deep learning), company size, educational background (Master's or Ph.D. in statistics, computer science, or data science), experience & seniority, geographic location
Salary determinants: Technical proficiency (ML algorithms, deep learning frameworks like TensorFlow, PyTorch, programming languages like Python, R), experience level, portfolio and projects, industry demand, company size, geographic location
The salaries for data roles, especially in finance and healthcare sectors, are higher ranging from mid to high six figures and sometimes even crossing into seven-figure territory for senior positions like Chief Data Officers. Even roles like Data Engineer and Data Scientist are routinely above 200K.
Companies hiring for data roles need to know that salaries vary a lot depending on the job and where it's located. It's important to understand the local tech scene, how expensive it is to live there, and how many other companies are hiring similar talent.
There’s a synchronized market demand for data expertise regardless of the company's maturity level, indicating the integral role of data professionals in driving business success across all phases of growth. Be prepared to invest heavily as your company grows!
Salaries can go up a lot as the team gets bigger. We might think salaries just go up a bit with more people, but this data shows they can actually rise quite a bit. It's not just about team size though; factors like how big the company is and how much it values data can also affect salaries in surprising ways.
Despite advancements in workplace equality, there's still a notable gap in salaries between men and women in data-related positions. The difference is stark in leadership roles.
Despite advancements in workplace equality, there's still a notable gap in salaries between men and women in data-related positions. The difference is stark in leadership roles.
Data professionals with ML, AI, and cloud computing skills tend to earn more. Industry recognised certifications such as AWS Certified Big Data – Specialty and Google Professional Data Engineer certification are also highly valued.
Proficiency in Python, R, SQL, and Apache Spark correlates with higher average salaries across data roles. Employers prioritise candidates with demonstrated expertise in these tools for roles involving data analysis, modeling, & visualization.
Approximately 75% of the overall data expenditure is allocated towards salaries, making it the most significant cost component. This includes a range of roles, from junior data analysts to senior data directors and chief data officers.
Benefits typically account for 18% of the total data expenditure. This includes expenses related to health insurance, retirement plans, bonuses, and other employee perks.
Training and development initiatives typically consume 3% of the total data expenditure. This includes costs associated with workshops, certifications, online courses, and professional development programs.
Investments in productivity and optimization strategies may vary but constitute 2% of the overall data expenditure.
Team-building initiatives usually represent a smaller portion of the budget, around 1% of the total data expenditure, and aim to foster a positive team culture and improve collaboration.
The cost of retention strategies, including initiatives to reduce turnover and attrition, may range from 1% of the total data expenditure, depending on the effectiveness of the implemented programs.
ML Engineer: Highly sought after in today's market due to their expertise in machine learning, commanding a median salary of $225K, reflecting the value placed on their specialized skills and contributions to data-driven innovation.
Datae Engineers: By volume of roles data engineers still emerge as top role among junior to mid-level roles, with a median pay of $180K, underscoring the strategic importance of their role in designing and managing the fundamental data infrastructure critical for organizational success and growth.
Certified Analytics Professional (CAP)
Cloudera Certified Professional (CCP) Data Engineer
Microsoft Certified: Azure Data Scientist Associate
Google Cloud Certified - Professional Data Engineer
AWS Certified Big Data - Specialty
Given the rising cost of hiring in-house data teams, 5X is a long term cost-effective and flexible alternative.
This option is popular among companies lacking an in-house data team. Using our 5X platform for consultancy services allows you to operate "data as a service" without the need to develop internal expertise. Companies can choose to continue with this arrangement long-term or transition into a hybrid model, blending in-house capabilities with 5X Team. We also assist with hiring and filling in-house roles as needed.
Our most popular option, good for companies with an existing data team. Companies use one of their head counts to bring on a 5X resource - which gives access to the entire portfolio of specialized services we can offer and addresses the challenge of accessing all necessary skill sets with a smaller team. Some of these skills include architecture, data engineering, analysis, roadmap planning, cost optimization, and AI.
This service is designed for flexibility. Used for high-impact projects, such as migrations or peak seasons, where specific expertise is required. It's a popular choice for companies needing immediate support for targeted initiatives.
When you choose 5X consultancy, you sign-up for your choice of:
Data leads/architect
Data engineers/Analytics engineers
Data analysts
Fractional chief data officer/data strategy
Data scientists/machine learning engineers (Just Launched)
Current state analysis
Long term data
Ad hoc data requests
Roadmap and business planning
Metrics & dashboard implementation
Cost & performance optimization
Hiring & interviewing
Data team and business user training
Word from our happy client…
“I’ve never been excited about a consultancy. I’ve worked with 3 different 5X engineers and each one is better than the past. My data team is 5X.”
Don’t let hefty data salaries hold you back.
Try 5X’s premium data services at 80% savings.
“There’s definitely a surge in data job popularity, with many transitioning from other tech roles to data roles and universities offering specialized data degrees, the supply is abundant. I would say the quantity may be high, the quality isn't. “
“Hiring for data roles was very challenging three years ago, but the market has stabilized and companies are focusing on upskilling existing staff.”
“There’s definitely a surge in data job popularity, with many transitioning from other tech roles to data roles and universities offering specialized data degrees, the supply is abundant. I would say the quantity may be high, the quality isn't. “
“Data's value soared post-pandemic, leading to investment, salary increases, and hiring hurdles. Growth may slow, but data skills remain highly sought after.”
“Until two years ago, we saw salaries rising rapidly, especially for data engineering and governance roles. During 2022, we observed a 10% increase in salaries offered from January to December.”
“Collaborate closely with executives like VP, directors, or CTO to align the data team's needs with the company's growth objectives. Develop a strategic plan that outlines the budget required to support specific headcounts and data initiatives.”
"Creating a data team is a strategic decision addressing a certain challenge. Budget justification comes from a process that cannot be done without data. Start with the current situation, such as errors in spreadsheets, and invest appropriately based on solid use cases."
“Depends on the company size but you need to define clear goals. People say, “hey, let's have a data team & we'll figure out what they’ll do.” That's like putting the horse behind the cart. When starting a new team, pick simple projects, iterate, & scale. Hire generalists who can wear multiple hats, from DE to analytics and ML.”
“Understanding the business and its processes is key. It helps identify data analytics team requirements based on business needs and team capabilities. Also, starting small allows for scalable growth.”
"I started with two people from the BI team and scaled to 12. First, align with stakeholders to understand their data value goals. Evaluate current processes and replace spreadsheets with applicable data tools. Eventually, adopt data platform technology and data governance tools."
“Build a data team in stages: 1. Manager with team-building experience. 2. Data engineers with business acumen. 3. Analytics engineers for translating needs. 4. Advanced specialists for departmental collaboration.”
“High-performing candidates excel in technical skills (data structures, SQL) and soft skills (business acumen, problem-solving). They leverage a broad spectrum of data tools to solve complex business problems and drive tangible outcomes.”
"The key factor is probably the willingness to learn. There's a misconception in the industry that you need a background in math or STEM. In reality, everything data analytics teams do, including data science, can be learned on the job. So, the willingness to learn is crucial."
"In today's job market, resumes have become indistinguishable, filled with similar projects and ubiquitous GitHub profiles. What once made candidates stand out now fades into the background. However, valuable internships and unique, self-driven projects sets a candidate apart."
"I prioritize curiosity and adaptability over specific skill sets. I look for candidates who are naturally curious and eager to learn on the job. I value problem-solving enthusiasm over expertise in a particular tool or field."
“Show how data initiatives impact decision-making and business outcomes, such as optimizing marketing spend to increase revenue. Highlight improvements in operational efficiency and revenue generation attributable to data-driven insights.”
“Outline two ROI approaches for data teams: quantitative, emphasizing metrics-driven results like revenue from data initiatives, and qualitative, highlighting strategic impacts on decisions like market entry or pricing. Success stories, quantitatively and qualitatively, are crucial for showcasing the team's value to the organization.”
“Marks & Spencer aligned data team goals with business objectives to demonstrate ROI. We focused on revenue growth and efficiency gains (e.g., time saved through automation) using KPIs that measured incremental revenue and efficiency improvements.”
“CFOs find it harder to justify data team investments compared to IT compliance (e.g., GDPR). Quantifying platform improvements is tougher than valuing tangible solutions like a chatbot.”
"When determining when to scale up your team, there's no one-size-fits-all answer. Recruitment can be time-consuming, often diverting focus from driving impact. It's crucial to strike a balance between building team capacity and delivering value."
“Startups often delay data team scaling until after funding rounds. We prioritized building internal data expertise through self-service tools and training for long-term cost-efficiency.”
“Resource scarcity hindering business demands is a sign to scale your data team. This includes rising customer insight requests, overloaded staff, and new projects requiring more personnel.”
“Team happiness and engagement are key to retention. Focus on fulfillment, autonomy (including flexible work and ownership of solutions) to keep them.”
“I clearly communicate expectations with my team. We discuss career goals, timelines, and best fit roles. This may involve internal moves or even them leaving the team.”
“Ensure good growth, ensure good learning, ensure good compensation, these are the three things that are essentials. And I would add a fourth one into the mix of good culture. People thrive in good culture, not just at a team level but at company level.”
“Support team growth (knowing they won't stay forever). Balance current satisfaction with development opportunities.”
"Hire people that are smarter than you. And keep them engaged doing interesting stuff. You won't be disappointed."
“Early in my career, we focused on fancy tech for tech's sake. Now I realize stakeholders only care about results. Focus on output, not the inner workings.”
“Don't run and make sure you accelerate and you start slow. For my next time when I'm trying to grow our team, what I'm gonna make sure of is that I'm really focusing on that value delivery and sort of growing slowly to begin with.
“Align short-term goals (2 years) with long-term vision (10 years). Ask: Are actions, even small ones, moving the team and business towards the long-term vision?”
“Data leaders are people leaders. Your team's success is your success. Focus on motivating and empowering them.”
"One of the hardest things about being a new data leader is delegation and understanding how to do it. You need to give that work up yourself and feel comfortable handing something off to someone and not micromanaging them. Thinking about how to make that jump between doing everything yourself and being comfortable delegating things."
